[BugFix] Fix false assertion with spec-decode=[2,4,..] and TP>2 (#29036 )

Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> (cherry picked from commit 8f4f77a727)
[BugFix] Ray with multiple nodes (#28873 )
2025-11-19 14:11:21 -08:00 · 2025-11-19 14:11:08 -08:00 · 2025-11-19 14:10:50 -08:00 · 2025-11-19 14:10:37 -08:00
2038 changed files with 58788 additions and 156284 deletions
--- a/.buildkite/ci_config.yaml
+++ b/.buildkite/ci_config.yaml
@@ -1,24 +0,0 @@
-name: vllm_ci
-job_dirs:
-  - ".buildkite/test_areas"
-  - ".buildkite/image_build"
-run_all_patterns:
-  - "docker/Dockerfile"
-  - "CMakeLists.txt"
-  - "requirements/common.txt"
-  - "requirements/cuda.txt"
-  - "requirements/build.txt"
-  - "requirements/test.txt"
-  - "setup.py"
-  - "csrc/"
-  - "cmake/"
-run_all_exclude_patterns:
-  - "docker/Dockerfile."
-  - "csrc/cpu/"
-  - "csrc/rocm/"
-  - "cmake/hipify.py"
-  - "cmake/cpu_extension.cmake"
-registries: public.ecr.aws/q9t5s3a7
-repositories:
-  main: "vllm-ci-postmerge-repo"
-  premerge: "vllm-ci-test-repo"
--- a/.buildkite/generate_index.py
+++ b/.buildkite/generate_index.py
@@ -0,0 +1,46 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import argparse
+import os
+
+template = """<!DOCTYPE html>
+<html>
+    <body>
+    <h1>Links for vLLM</h1/>
+        <a href="../{x86_wheel_html_escaped}">{x86_wheel}</a><br/>
+        <a href="../{arm_wheel_html_escaped}">{arm_wheel}</a><br/>
+    </body>
+</html>
+"""
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--wheel", help="The wheel path.", required=True)
+args = parser.parse_args()
+
+filename = os.path.basename(args.wheel)
+
+with open("index.html", "w") as f:
+    print(f"Generated index.html for {args.wheel}")
+    # sync the abi tag with .buildkite/scripts/upload-wheels.sh
+    if "x86_64" in filename:
+        x86_wheel = filename
+        arm_wheel = filename.replace("x86_64", "aarch64").replace(
+            "manylinux1", "manylinux2014"
+        )
+    elif "aarch64" in filename:
+        x86_wheel = filename.replace("aarch64", "x86_64").replace(
+            "manylinux2014", "manylinux1"
+        )
+        arm_wheel = filename
+    else:
+        raise ValueError(f"Unsupported wheel: {filename}")
+    # cloudfront requires escaping the '+' character
+    f.write(
+        template.format(
+            x86_wheel=x86_wheel,
+            x86_wheel_html_escaped=x86_wheel.replace("+", "%2B"),
+            arm_wheel=arm_wheel,
+            arm_wheel_html_escaped=arm_wheel.replace("+", "%2B"),
+        )
+    )
--- a/.buildkite/image_build/image_build.sh
+++ b/.buildkite/image_build/image_build.sh
@@ -1,56 +0,0 @@
-#!/bin/bash
-set -e
-
-if [[ $# -lt 8 ]]; then
-  echo "Usage: $0 <registry> <repo> <commit> <branch> <vllm_use_precompiled> <vllm_merge_base_commit> <cache_from> <cache_to>"
-  exit 1
-fi
-
-REGISTRY=$1
-REPO=$2
-BUILDKITE_COMMIT=$3
-BRANCH=$4
-VLLM_USE_PRECOMPILED=$5
-VLLM_MERGE_BASE_COMMIT=$6
-CACHE_FROM=$7
-CACHE_TO=$8
-
-# authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $REGISTRY
-aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
-
-# docker buildx 
-docker buildx create --name vllm-builder --driver docker-container --use
-docker buildx inspect --bootstrap
-docker buildx ls
-
-# skip build if image already exists
-if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT) ]]; then
-  echo "Image not found, proceeding with build..."
-else
-  echo "Image found"
-  exit 0
-fi
-
-if [[ "${VLLM_USE_PRECOMPILED:-0}" == "1" ]]; then
-  merge_base_commit_build_args="--build-arg VLLM_MERGE_BASE_COMMIT=${VLLM_MERGE_BASE_COMMIT}"
-else
-  merge_base_commit_build_args=""
-fi
-
-# build
-docker buildx build --file docker/Dockerfile \
-  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
-  --build-arg USE_SCCACHE=1 \
-  --build-arg TORCH_CUDA_ARCH_LIST="8.0 8.9 9.0 10.0" \
-  --build-arg FI_TORCH_CUDA_ARCH_LIST="8.0 8.9 9.0a 10.0a" \
-  --build-arg VLLM_USE_PRECOMPILED="${VLLM_USE_PRECOMPILED:-0}" \
-  ${merge_base_commit_build_args} \
-  --cache-from type=registry,ref=${CACHE_FROM},mode=max \
-  --cache-to type=registry,ref=${CACHE_TO},mode=max \
-  --tag ${REGISTRY}/${REPO}:${BUILDKITE_COMMIT} \
-  $( [[ "${BRANCH}" == "main" ]] && echo "--tag ${REGISTRY}/${REPO}:latest" ) \
-  --push \
-  --target test \
-  --progress plain .
--- a/.buildkite/image_build/image_build.yaml
+++ b/.buildkite/image_build/image_build.yaml
@@ -1,57 +0,0 @@
-group: Abuild
-steps:
-  - label: ":docker: Build image"
-    key: image-build
-    depends_on: []
-    commands:
-    - .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $VLLM_USE_PRECOMPILED $VLLM_MERGE_BASE_COMMIT $CACHE_FROM $CACHE_TO
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 2
-        - exit_status: -10  # Agent was lost
-          limit: 2
-
-  - label: ":docker: Build CPU image"
-    key: image-build-cpu
-    depends_on: []
-    commands:
-    - .buildkite/image_build/image_build_cpu.sh $REGISTRY $REPO $BUILDKITE_COMMIT
-    env:
-      DOCKER_BUILDKIT: "1"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 2
-        - exit_status: -10  # Agent was lost
-          limit: 2
-
-  - label: ":docker: Build HPU image"
-    soft_fail: true
-    depends_on: []
-    key: image-build-hpu
-    commands:
-    - .buildkite/image_build/image_build_hpu.sh $REGISTRY $REPO $BUILDKITE_COMMIT
-    env:
-      DOCKER_BUILDKIT: "1"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 2
-        - exit_status: -10  # Agent was lost
-          limit: 2
-  
-  - label: ":docker: Build CPU arm64 image"
-    key: cpu-arm64-image-build
-    depends_on: []
-    optional: true
-    commands:
-    - .buildkite/image_build/image_build_cpu_arm64.sh $REGISTRY $REPO $BUILDKITE_COMMIT
-    env:
-      DOCKER_BUILDKIT: "1"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 2
-        - exit_status: -10  # Agent was lost
-          limit: 2
--- a/.buildkite/image_build/image_build_cpu.sh
+++ b/.buildkite/image_build/image_build_cpu.sh
@@ -1,36 +0,0 @@
-#!/bin/bash
-set -e
-
-if [[ $# -lt 3 ]]; then
-  echo "Usage: $0 <registry> <repo> <commit>"
-  exit 1
-fi
-
-REGISTRY=$1
-REPO=$2
-BUILDKITE_COMMIT=$3
-
-# authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $REGISTRY
-
-# skip build if image already exists
-if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu) ]]; then
-  echo "Image not found, proceeding with build..."
-else
-  echo "Image found"
-  exit 0
-fi
-
-# build
-docker build --file docker/Dockerfile.cpu \
-  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
-  --build-arg VLLM_CPU_AVX512BF16=true \
-  --build-arg VLLM_CPU_AVX512VNNI=true \
-  --build-arg VLLM_CPU_AMXBF16=true \
-  --tag $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu \
-  --target vllm-test \
-  --progress plain .
-
-# push
-docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu
--- a/.buildkite/image_build/image_build_cpu_arm64.sh
+++ b/.buildkite/image_build/image_build_cpu_arm64.sh
@@ -1,33 +0,0 @@
-#!/bin/bash
-set -e
-
-if [[ $# -lt 3 ]]; then
-  echo "Usage: $0 <registry> <repo> <commit>"
-  exit 1
-fi
-
-REGISTRY=$1
-REPO=$2
-BUILDKITE_COMMIT=$3
-
-# authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $REGISTRY
-
-# skip build if image already exists
-if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu) ]]; then
-  echo "Image not found, proceeding with build..."
-else
-  echo "Image found"
-  exit 0
-fi
-
-# build
-docker build --file docker/Dockerfile.cpu \
-  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
-  --tag $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu \
-  --target vllm-test \
-  --progress plain .
-
-# push
-docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu
--- a/.buildkite/image_build/image_build_hpu.sh
+++ b/.buildkite/image_build/image_build_hpu.sh
@@ -1,34 +0,0 @@
-#!/bin/bash
-set -e
-
-if [[ $# -lt 3 ]]; then
-  echo "Usage: $0 <registry> <repo> <commit>"
-  exit 1
-fi
-
-REGISTRY=$1
-REPO=$2
-BUILDKITE_COMMIT=$3
-
-# authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $REGISTRY
-
-# skip build if image already exists
-if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu) ]]; then
-  echo "Image not found, proceeding with build..."
-else
-  echo "Image found"
-  exit 0
-fi
-
-# build
-docker build \
-  --file tests/pytorch_ci_hud_benchmark/Dockerfile.hpu \
-  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
-  --tag $REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu \
-  --progress plain \
-  https://github.com/vllm-project/vllm-gaudi.git
-
-# push
-docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu
--- a/.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml
+++ b/.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml
@@ -8,4 +8,3 @@ tasks:
    value: 0.80
 limit: 250 # will run on 250 * 14 subjects = 3500 samples
 num_fewshot: 5
-rtol: 0.05
--- a/.buildkite/lm-eval-harness/configs/models-large-rocm.txt
+++ b/.buildkite/lm-eval-harness/configs/models-large-rocm.txt
@@ -1 +0,0 @@
-Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml
--- a/.buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh
+++ b/.buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh
@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on chartqa for vllm.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.9.2"
+#   pip install lm-eval==0.4.9

 usage() {
    echo``
--- a/.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh
+++ b/.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh
@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on GSM for transformers.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.9.2"
+#   pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]

 usage() {
    echo``
--- a/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh
+++ b/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh
@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.9.2"
+#   pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]

 usage() {
    echo``
--- a/.buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh
+++ b/.buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh
@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.9.2"
+#   pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]

 usage() {
    echo``
--- a/.buildkite/lm-eval-harness/test_lm_eval_correctness.py
+++ b/.buildkite/lm-eval-harness/test_lm_eval_correctness.py
@@ -9,40 +9,11 @@ pytest -s -v test_lm_eval_correctness.py \
    --tp-size=1
 """

-import os
-from contextlib import contextmanager
-
 import lm_eval
 import numpy as np
 import yaml

-DEFAULT_RTOL = 0.08
-
-
-@contextmanager
-def scoped_env_vars(new_env: dict[str, str]):
-    if not new_env:
-        # Fast path: nothing to do
-        yield
-        return
-
-    old_values = {}
-    new_keys = []
-
-    try:
-        for key, value in new_env.items():
-            if key in os.environ:
-                old_values[key] = os.environ[key]
-            else:
-                new_keys.append(key)
-            os.environ[key] = str(value)
-        yield
-    finally:
-        # Restore / clean up
-        for key, value in old_values.items():
-            os.environ[key] = value
-        for key in new_keys:
-            os.environ.pop(key, None)
+RTOL = 0.08


 def launch_lm_eval(eval_config, tp_size):
@@ -60,28 +31,24 @@ def launch_lm_eval(eval_config, tp_size):
        f"add_bos_token=true,"
        f"trust_remote_code={trust_remote_code},"
        f"max_model_len={max_model_len},"
-        "allow_deprecated_quantization=True,"
    )
-
-    env_vars = eval_config.get("env_vars", None)
-    with scoped_env_vars(env_vars):
-        results = lm_eval.simple_evaluate(
-            model=backend,
-            model_args=model_args,
-            tasks=[task["name"] for task in eval_config["tasks"]],
-            num_fewshot=eval_config["num_fewshot"],
-            limit=eval_config["limit"],
-            # TODO(yeq): using chat template w/ fewshot_as_multiturn is supposed help
-            # text models. however, this is regressing measured strict-match for
-            # existing text models in CI, so only apply it for mm, or explicitly set
-            apply_chat_template=eval_config.get(
-                "apply_chat_template", backend == "vllm-vlm"
-            ),
-            fewshot_as_multiturn=eval_config.get("fewshot_as_multiturn", False),
-            # Forward decoding and early-stop controls (e.g., max_gen_toks, until=...)
-            gen_kwargs=eval_config.get("gen_kwargs"),
-            batch_size=batch_size,
-        )
+    results = lm_eval.simple_evaluate(
+        model=backend,
+        model_args=model_args,
+        tasks=[task["name"] for task in eval_config["tasks"]],
+        num_fewshot=eval_config["num_fewshot"],
+        limit=eval_config["limit"],
+        # TODO(yeq): using chat template w/ fewshot_as_multiturn is supposed help
+        # text models. however, this is regressing measured strict-match for
+        # existing text models in CI, so only apply it for mm, or explicitly set
+        apply_chat_template=eval_config.get(
+            "apply_chat_template", backend == "vllm-vlm"
+        ),
+        fewshot_as_multiturn=eval_config.get("fewshot_as_multiturn", False),
+        # Forward decoding and early-stop controls (e.g., max_gen_toks, until=...)
+        gen_kwargs=eval_config.get("gen_kwargs"),
+        batch_size=batch_size,
+    )
    return results


@@ -90,8 +57,6 @@ def test_lm_eval_correctness_param(config_filename, tp_size):

    results = launch_lm_eval(eval_config, tp_size)

-    rtol = eval_config.get("rtol", DEFAULT_RTOL)
-
    success = True
    for task in eval_config["tasks"]:
        for metric in task["metrics"]:
@@ -99,9 +64,8 @@ def test_lm_eval_correctness_param(config_filename, tp_size):
            measured_value = results["results"][task["name"]][metric["name"]]
            print(
                f"{task['name']} | {metric['name']}: "
-                f"ground_truth={ground_truth:.3f} | "
-                f"measured={measured_value:.3f} | rtol={rtol}"
+                f"ground_truth={ground_truth} | measured={measured_value}"
            )
-            success = success and np.isclose(ground_truth, measured_value, rtol=rtol)
+            success = success and np.isclose(ground_truth, measured_value, rtol=RTOL)

    assert success
--- a/.buildkite/performance-benchmarks/README.md
+++ b/.buildkite/performance-benchmarks/README.md
@@ -7,7 +7,7 @@ vLLM also maintains a continuous performance benchmark under [perf.vllm.ai](http

 ## Performance benchmark quick overview

-**Benchmarking Coverage**: latency, throughput and fix-qps serving on B200, A100, H100, Intel® Xeon® Processors, Intel® Gaudi® 3 Accelerators and Arm® Neoverse™ with different models.
+**Benchmarking Coverage**: latency, throughput and fix-qps serving on B200, A100, H100, Intel® Xeon® Processors and Intel® Gaudi® 3 Accelerators with different models.

 **Benchmarking Duration**: about 1hr.

@@ -23,7 +23,7 @@ bash .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh

 Runtime environment variables:

- `ON_CPU`: set the value to '1' on Intel® Xeon® and Arm® Neoverse™ Processors. Default value is 0.
+- `ON_CPU`: set the value to '1' on Intel® Xeon® Processors. Default value is 0.
 - `SERVING_JSON`: JSON file to use for the serving tests. Default value is empty string (use default file).
 - `LATENCY_JSON`: JSON file to use for the latency tests. Default value is empty string (use default file).
 - `THROUGHPUT_JSON`: JSON file to use for the throughout tests. Default value is empty string (use default file).
@@ -34,9 +34,8 @@ Runtime environment variables:

 See [performance-benchmarks-descriptions.md](performance-benchmarks-descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
 > NOTE: For Intel® Xeon® Processors, use `tests/latency-tests-cpu.json`, `tests/throughput-tests-cpu.json`, `tests/serving-tests-cpu.json` instead.
-> For Intel® Gaudi® 3 Accelerators, use `tests/latency-tests-hpu.json`, `tests/throughput-tests-hpu.json`, `tests/serving-tests-hpu.json` instead.
-> For Arm® Neoverse™, use `tests/latency-tests-arm64-cpu.json`, `tests/throughput-tests-arm64-cpu.json`, `tests/serving-tests-arm64-cpu.json` instead.
-
+For Intel® Gaudi® 3 Accelerators, use `tests/latency-tests-hpu.json`, `tests/throughput-tests-hpu.json`, `tests/serving-tests-hpu.json` instead.
+>
 ### Latency test

 Here is an example of one test inside `latency-tests.json`:
@@ -109,65 +108,6 @@ The number of this test is less stable compared to the delay and latency benchma

 WARNING: The benchmarking script will save json results by itself, so please do not configure `--save-results` or other results-saving-related parameters in `serving-tests.json`.

-#### Default Parameters Field
-
-We can specify default parameters in a JSON field with key `defaults`. Parameters defined in the field are applied globally to all serving tests, and can be overridden in test case fields. Here is an example:
-
-<details>
-<summary> An Example of default parameters field </summary>
-
-```json
-{
-  "defaults": {
-    "qps_list": [
-      "inf"
-    ],
-    "server_environment_variables": {
-      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1
-    },
-    "server_parameters": {
-      "tensor_parallel_size": 1,
-      "dtype": "bfloat16",
-      "block_size": 128,
-      "disable_log_stats": "",
-      "load_format": "dummy"
-    },
-    "client_parameters": {
-      "backend": "vllm",
-      "dataset_name": "random",
-      "random-input-len": 128,
-      "random-output-len": 128,
-      "num_prompts": 200,
-      "ignore-eos": ""
-    }
-  },
-  "tests": [
-    {
-      "test_name": "serving_llama3B_tp2_random_128_128",
-      "server_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct",
-        "tensor_parallel_size": 2,
-      },
-      "client_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct",
-      }
-    },
-    {
-      "test_name": "serving_qwen3_tp4_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-14B",
-        "tensor_parallel_size": 4,
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-14B",
-      }
-    },
-  ]
-}
-```
-
-</details>
-
 ### Visualizing the results

 The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](performance-benchmarks-descriptions.md) with real benchmarking results.
@@ -176,6 +116,19 @@ If you do not see the table, please wait till the benchmark finish running.
 The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
 The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.

-#### Performance Results Comparison  
+The `compare-json-results.py` helps to compare benchmark results JSON files converted using `convert-results-json-to-markdown.py`.
+When run, benchmark script generates results under `benchmark/results` folder, along with the `benchmark_results.md` and `benchmark_results.json`.
+`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.  
+If only one benchmark_results.json is passed, `compare-json-results.py` compares different TP and PP configurations in the benchmark_results.json instead.

-Follow the instructions in [performance results comparison](https://docs.vllm.ai/en/latest/benchmarking/dashboard/#performance-results-comparison) to analyze performance results and the sizing guide.
+Here is an example using the script to compare result_a and result_b with Model, Dataset name, input/output length, max concurrency and qps.
+`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
+
+|   | Model | Dataset Name | Input Len | Output Len | # of max concurrency | qps  | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
+|----|---------------------------------------|--------|-----|-----|------|-----|-----------|----------|----------|
+| 0  | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | 1 | 142.633982                             | 156.526018                             | 1.097396 |
+| 1  | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | inf| 241.620334                             | 294.018783                             | 1.216863 |
+
+A comparison diagram will be generated below the table.
+Here is an example to compare between 96c/results_gnr_96c_091_tp2pp3 and 128c/results_gnr_128c_091_tp2pp3
+<img width="1886" height="828" alt="image" src="https://github.com/user-attachments/assets/c02a43ef-25d0-4fd6-90e5-2169a28682dd" />
--- a/.buildkite/performance-benchmarks/scripts/compare-json-results.py
+++ b/.buildkite/performance-benchmarks/scripts/compare-json-results.py
@@ -1,13 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
 import argparse
-import html as _html
 import json
 import os
-from dataclasses import dataclass
 from importlib import util

 import pandas as pd
@@ -15,49 +10,27 @@ import pandas as pd
 pd.options.display.float_format = "{:.2f}".format
 plotly_found = util.find_spec("plotly.express") is not None

-DEFAULT_INFO_COLS = [
-    "Model",
-    "Dataset Name",
-    "Input Len",
-    "Output Len",
-    #    "TP Size",
-    #    "PP Size",
-    "# of max concurrency.",
-    "qps",
-]

-# Safety net: if any DataFrame leaks into to_html(), keep precision at 2.
-pd.set_option("display.precision", 2)
-pd.set_option("display.float_format", lambda x: f"{x:.2f}")
-
-
-# -----------------------------
-# Core data compare
-# -----------------------------
 def compare_data_columns(
-    files: list[str],
-    name_column: str,
-    data_column: str,
-    info_cols: list[str],
-    drop_column: str,
-    debug: bool = False,
+    files, name_column, data_column, info_cols, drop_column, debug=False
 ):
    """
    Align concatenation by keys derived from info_cols instead of row order.
    - Pick one canonical key list: subset of info_cols present in ALL files.
    - For each file: set index to those keys, aggregate duplicates
-      (mean for metric, first for names).
+    - (mean for metric, first for names).
    - Concat along axis=1 (indexes align), then reset_index so callers can
-      group by columns.
+    - group by columns.
    - If --debug, add a <file_label>_name column per file.
    """
    print("\ncompare_data_column:", data_column)

    frames = []
-    raw_data_cols: list[str] = []
+    raw_data_cols = []
    compare_frames = []

-    cols_per_file: list[set] = []
+    # 1) choose a canonical key list from info_cols that exists in ALL files
+    cols_per_file = []
    for f in files:
        try:
            df_tmp = pd.read_json(f, orient="records")
@@ -67,20 +40,24 @@ def compare_data_columns(

    key_cols = [c for c in info_cols if all(c in cset for cset in cols_per_file)]
    if not key_cols:
+        # soft fallback: use any info_cols present in the first file
        key_cols = [c for c in info_cols if c in list(cols_per_file[0])]
    if not key_cols:
        raise ValueError(
            "No common key columns found from info_cols across the input files."
        )

+    # 2) build a single "meta" block (keys as columns) once, aligned by the key index
    meta_added = False

    for file in files:
        df = pd.read_json(file, orient="records")

+        # Keep rows that actually have the compared metric (same as original behavior)
        if drop_column in df.columns:
            df = df.dropna(subset=[drop_column], ignore_index=True)

+        # Stabilize numeric key columns (harmless if missing)
        for c in (
            "Input Len",
            "Output Len",
@@ -92,26 +69,32 @@ def compare_data_columns(
            if c in df.columns:
                df[c] = pd.to_numeric(df[c], errors="coerce")

+        # Ensure all key columns exist
        for c in key_cols:
            if c not in df.columns:
                df[c] = pd.NA

+        # Set index = key_cols and aggregate duplicates → unique MultiIndex
        df_idx = df.set_index(key_cols, drop=False)

+        # meta (key columns), unique per key
        meta = df_idx[key_cols]
        if not meta.index.is_unique:
            meta = meta.groupby(level=key_cols, dropna=False).first()

+        # metric series for this file, aggregated to one row per key
        file_label = "/".join(file.split("/")[:-1]) or os.path.basename(file)
        s = df_idx[data_column]
        if not s.index.is_unique:
            s = s.groupby(level=key_cols, dropna=False).mean()
-        s.name = file_label
+        s.name = file_label  # column label like original

+        # add meta once (from first file) so keys are the leftmost columns
        if not meta_added:
            frames.append(meta)
            meta_added = True

+        # (NEW) debug: aligned test-name column per file
        if debug and name_column in df_idx.columns:
            name_s = df_idx[name_column]
            if not name_s.index.is_unique:
@@ -123,19 +106,26 @@ def compare_data_columns(
        raw_data_cols.append(file_label)
        compare_frames.append(s)

+        # Generalize ratio: for any file N>=2, add ratio (fileN / file1)
        if len(compare_frames) >= 2:
            base = compare_frames[0]
            current = compare_frames[-1]
            if "P99" in data_column or "Median" in data_column:
-                ratio = base / current
+                ratio = base / current  # for latency
            else:
                ratio = current / base
-            ratio = ratio.mask(base == 0)
+            ratio = ratio.mask(base == 0)  # avoid inf when baseline is 0
            ratio.name = f"Ratio 1 vs {len(compare_frames)}"
            frames.append(ratio)

-    concat_df = pd.concat(frames, axis=1).reset_index(drop=True)
+    # 4) concat on columns with aligned MultiIndex;
+    # then reset_index to return keys as columns
+    concat_df = pd.concat(frames, axis=1)
+    concat_df = concat_df.reset_index(drop=True).reset_index()
+    if "index" in concat_df.columns:
+        concat_df = concat_df.drop(columns=["index"])

+    # Ensure key/info columns appear first (in your info_cols order)
    front = [c for c in info_cols if c in concat_df.columns]
    rest = [c for c in concat_df.columns if c not in front]
    concat_df = concat_df[front + rest]
@@ -144,15 +134,20 @@ def compare_data_columns(
    return concat_df, raw_data_cols


-# -----------------------------
-# Split helper
-# -----------------------------
 def split_json_by_tp_pp(
    input_file: str = "benchmark_results.json", output_root: str = "."
 ) -> list[str]:
+    """
+    Split a benchmark JSON into separate folders by (TP Size, PP Size).
+
+    Creates: <output_root>/tp{TP}_pp{PP}/benchmark_results.json
+    Returns: list of file paths written.
+    """
+    # Load JSON data into DataFrame
    with open(input_file, encoding="utf-8") as f:
        data = json.load(f)

+    # If the JSON is a dict with a list under common keys, use that list
    if isinstance(data, dict):
        for key in ("results", "serving_results", "benchmarks", "data"):
            if isinstance(data.get(key), list):
@@ -161,6 +156,7 @@ def split_json_by_tp_pp(

    df = pd.DataFrame(data)

+    # Keep only "serving" tests
    name_col = next(
        (c for c in ["Test name", "test_name", "Test Name"] if c in df.columns), None
    )
@@ -169,6 +165,7 @@ def split_json_by_tp_pp(
            df[name_col].astype(str).str.contains(r"serving", case=False, na=False)
        ].copy()

+    # Handle alias column names
    rename_map = {
        "tp_size": "TP Size",
        "tensor_parallel_size": "TP Size",
@@ -179,14 +176,21 @@ def split_json_by_tp_pp(
        columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True
    )

+    # Ensure TP/PP columns exist (default to 1 if missing)
    if "TP Size" not in df.columns:
        df["TP Size"] = 1
    if "PP Size" not in df.columns:
        df["PP Size"] = 1

-    df["TP Size"] = pd.to_numeric(df["TP Size"], errors="coerce").fillna(1).astype(int)
-    df["PP Size"] = pd.to_numeric(df["PP Size"], errors="coerce").fillna(1).astype(int)
+    # make sure TP/PP are numeric ints with no NaN
+    df["TP Size"] = (
+        pd.to_numeric(df.get("TP Size", 1), errors="coerce").fillna(1).astype(int)
+    )
+    df["PP Size"] = (
+        pd.to_numeric(df.get("PP Size", 1), errors="coerce").fillna(1).astype(int)
+    )

+    # Split into separate folders
    saved_paths: list[str] = []
    for (tp, pp), group_df in df.groupby(["TP Size", "PP Size"], dropna=False):
        folder_name = os.path.join(output_root, f"tp{int(tp)}_pp{int(pp)}")
@@ -199,9 +203,32 @@ def split_json_by_tp_pp(
    return saved_paths


-# -----------------------------
-# Styling helpers
-# -----------------------------
+def _add_limit_line(fig, y_value, label):
+    # Visible dashed line + annotation
+    fig.add_hline(
+        y=y_value,
+        line_dash="dash",
+        line_color="red" if "ttft" in label.lower() else "blue",
+        annotation_text=f"{label}: {y_value} ms",
+        annotation_position="top left",
+    )
+    # Optional: add a legend item (as a transparent helper trace)
+    if plot and plotly_found:
+        import plotly.graph_objects as go
+
+        fig.add_trace(
+            go.Scatter(
+                x=[None],
+                y=[None],
+                mode="lines",
+                line=dict(
+                    dash="dash", color="red" if "ttft" in label.lower() else "blue"
+                ),
+                name=f"{label}",
+            )
+        )
+
+
 def _find_concurrency_col(df: pd.DataFrame) -> str:
    for c in [
        "# of max concurrency.",
@@ -212,6 +239,7 @@ def _find_concurrency_col(df: pd.DataFrame) -> str:
    ]:
        if c in df.columns:
            return c
+    # Fallback: guess an integer-like column (harmless if unused)
    for c in df.columns:
        if df[c].dtype.kind in "iu" and df[c].nunique() > 1 and df[c].min() >= 1:
            return c
@@ -220,7 +248,8 @@ def _find_concurrency_col(df: pd.DataFrame) -> str:

 def _highlight_threshold(
    df: pd.DataFrame, threshold: float
-) -> pd.io.formats.style.Styler:
+) -> "pd.io.formats.style.Styler":
+    """Highlight numeric per-configuration columns with value <= threshold."""
    conc_col = _find_concurrency_col(df)
    key_cols = [
        c
@@ -231,7 +260,6 @@ def _highlight_threshold(
        c for c in df.columns if c not in key_cols and not str(c).startswith("Ratio")
    ]
    conf_cols = [c for c in conf_cols if pd.api.types.is_numeric_dtype(df[c])]
-
    return df.style.map(
        lambda v: "background-color:#e6ffe6;font-weight:bold;"
        if pd.notna(v) and v <= threshold
@@ -240,264 +268,7 @@ def _highlight_threshold(
    )


-def highlight_ratio_columns(styler: pd.io.formats.style.Styler):
-    ratio_cols = [c for c in styler.data.columns if "ratio" in str(c).lower()]
-    if not ratio_cols:
-        return styler
-
-    styler = styler.apply(
-        lambda _: ["background-color: #fff3b0"] * len(styler.data),
-        subset=ratio_cols,
-        axis=0,
-    )
-
-    styler = styler.set_table_styles(
-        [
-            {
-                "selector": f"th.col_heading.level0.col{i}",
-                "props": [("background-color", "#fff3b0")],
-            }
-            for i, col in enumerate(styler.data.columns)
-            if col in ratio_cols
-        ],
-        overwrite=False,
-    )
-    return styler
-
-
-def _apply_two_decimals(
-    styler: pd.io.formats.style.Styler,
-) -> pd.io.formats.style.Styler:
-    df = styler.data
-    num_cols = df.select_dtypes("number").columns
-    if len(num_cols) == 0:
-        return styler
-    return styler.format({c: "{:.2f}" for c in num_cols}, na_rep="")
-
-
-# -----------------------------
-# Valid max concurrency summary helpers
-# -----------------------------
-def _config_value_columns(df: pd.DataFrame, conc_col: str) -> list[str]:
-    key_cols = [
-        c
-        for c in ["Model", "Dataset Name", "Input Len", "Output Len"]
-        if c in df.columns
-    ]
-    exclude = set(key_cols + [conc_col, "qps", "QPS"])
-
-    cols: list[str] = []
-    for c in df.columns:
-        if c in exclude:
-            continue
-        lc = str(c).lower()
-        if lc.startswith("ratio"):
-            continue
-        if lc.endswith("_name") or lc == "test name" or lc == "test_name":
-            continue
-        if pd.api.types.is_numeric_dtype(df[c]):
-            cols.append(c)
-    return cols
-
-
-def _max_concurrency_ok(
-    df: pd.DataFrame, conc_col: str, cfg_col: str, threshold: float
-):
-    if df is None or conc_col not in df.columns or cfg_col not in df.columns:
-        return pd.NA
-
-    d = df[[conc_col, cfg_col]].copy()
-    d[conc_col] = pd.to_numeric(d[conc_col], errors="coerce")
-    d[cfg_col] = pd.to_numeric(d[cfg_col], errors="coerce")
-    d = d.dropna(subset=[conc_col, cfg_col])
-
-    if d.empty:
-        return pd.NA
-
-    ok = d[d[cfg_col] <= threshold]
-    if ok.empty:
-        return pd.NA
-
-    return ok[conc_col].max()
-
-
-def _value_at_concurrency(df: pd.DataFrame, conc_col: str, cfg_col: str, conc_value):
-    if (
-        df is None
-        or conc_col not in df.columns
-        or cfg_col not in df.columns
-        or pd.isna(conc_value)
-    ):
-        return pd.NA
-
-    d = df[[conc_col, cfg_col]].copy()
-    d[conc_col] = pd.to_numeric(d[conc_col], errors="coerce")
-    d[cfg_col] = pd.to_numeric(d[cfg_col], errors="coerce")
-
-    conc_value = pd.to_numeric(conc_value, errors="coerce")
-    if pd.isna(conc_value):
-        return pd.NA
-
-    hit = d[d[conc_col] == conc_value]
-    if hit.empty:
-        return pd.NA
-    return hit[cfg_col].iloc[0]
-
-
-def build_valid_max_concurrency_summary_html(
-    tput_group_df: pd.DataFrame | None,
-    ttft_group_df: pd.DataFrame | None,
-    tpot_group_df: pd.DataFrame | None,
-    conc_col: str,
-    args,
-) -> str:
-    if ttft_group_df is None and tpot_group_df is None:
-        return ""
-
-    ttft_cols = (
-        _config_value_columns(ttft_group_df, conc_col)
-        if ttft_group_df is not None
-        else []
-    )
-    tpot_cols = (
-        _config_value_columns(tpot_group_df, conc_col)
-        if tpot_group_df is not None
-        else []
-    )
-    tput_cols = (
-        _config_value_columns(tput_group_df, conc_col)
-        if tput_group_df is not None
-        else []
-    )
-
-    if ttft_group_df is not None and tpot_group_df is not None:
-        cfg_cols = [c for c in ttft_cols if c in tpot_cols]
-        if tput_group_df is not None:
-            cfg_cols = [c for c in cfg_cols if c in tput_cols] or cfg_cols
-    else:
-        cfg_cols = ttft_cols or tpot_cols
-
-    if not cfg_cols:
-        cfg_cols = sorted(set(ttft_cols) | set(tpot_cols) | set(tput_cols), key=str)
-
-    rows = []
-    for cfg in cfg_cols:
-        ttft_max = (
-            _max_concurrency_ok(ttft_group_df, conc_col, cfg, args.ttft_max_ms)
-            if ttft_group_df is not None
-            else pd.NA
-        )
-        tpot_max = (
-            _max_concurrency_ok(tpot_group_df, conc_col, cfg, args.tpot_max_ms)
-            if tpot_group_df is not None
-            else pd.NA
-        )
-        both = (
-            pd.NA
-            if (pd.isna(ttft_max) or pd.isna(tpot_max))
-            else min(ttft_max, tpot_max)
-        )
-
-        tput_at_both = (
-            _value_at_concurrency(tput_group_df, conc_col, cfg, both)
-            if tput_group_df is not None
-            else pd.NA
-        )
-        ttft_at_both = (
-            _value_at_concurrency(ttft_group_df, conc_col, cfg, both)
-            if ttft_group_df is not None
-            else pd.NA
-        )
-        tpot_at_both = (
-            _value_at_concurrency(tpot_group_df, conc_col, cfg, both)
-            if tpot_group_df is not None
-            else pd.NA
-        )
-
-        rows.append(
-            {
-                "Configuration": cfg,
-                f"Max {conc_col} (TTFT ≤ {args.ttft_max_ms:g} ms)": ttft_max,
-                f"Max {conc_col} (TPOT ≤ {args.tpot_max_ms:g} ms)": tpot_max,
-                f"Max {conc_col} (Both)": both,
-                "Output Tput @ Both (tok/s)": tput_at_both,
-                "TTFT @ Both (ms)": ttft_at_both,
-                "TPOT @ Both (ms)": tpot_at_both,
-            }
-        )
-
-    summary_df = pd.DataFrame(rows)
-
-    # --- Coerce numeric columns so Styler doesn't miss them due to object dtype ---
-    for c in summary_df.columns:
-        if c == "Configuration":
-            continue
-        summary_df[c] = pd.to_numeric(summary_df[c], errors="coerce")
-
-    both_col = f"Max {conc_col} (Both)"
-
-    # --- Strict 2-decimal formatting for ALL non-Configuration columns ---
-    formatters = {}
-    for c in summary_df.columns:
-        if c == "Configuration":
-            continue
-        # default argument binds per-column formatter correctly
-        formatters[c] = lambda v: "" if pd.isna(v) else f"{float(v):.2f}"
-
-    styler = summary_df.style.format(formatters)
-
-    def _green(v):
-        return "background-color:#e6ffe6;font-weight:bold;" if pd.notna(v) else ""
-
-    if both_col in summary_df.columns:
-        styler = styler.map(_green, subset=[both_col])
-
-    title = (
-        '<div style="font-size: 1.15em; font-weight: 700; margin: 12px 0 6px 0;">'
-        "Valid Max Concurrency Summary"
-        "</div>\n"
-    )
-    return title + styler.to_html(table_attributes='border="1" class="dataframe"')
-
-
-# -----------------------------
-# Plot helper
-# -----------------------------
-def _add_limit_line(fig, y_value: float, label: str):
-    fig.add_hline(
-        y=y_value,
-        line_dash="dash",
-        line_color="red" if "ttft" in label.lower() else "blue",
-        annotation_text=f"{label}: {y_value} ms",
-        annotation_position="top left",
-    )
-    if plotly_found:
-        import plotly.graph_objects as go
-
-        fig.add_trace(
-            go.Scatter(
-                x=[None],
-                y=[None],
-                mode="lines",
-                line=dict(
-                    dash="dash",
-                    color="red" if "ttft" in label.lower() else "blue",
-                ),
-                name=label,
-            )
-        )
-
-
-# -----------------------------
-# Refactored main + group-first report
-# -----------------------------
-@dataclass(frozen=True)
-class MetricPlan:
-    data_cols: list[str]
-    drop_column: str
-
-
-def build_parser() -> argparse.ArgumentParser:
+if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-f", "--file", action="append", type=str, help="input file name"
@@ -537,289 +308,149 @@ def build_parser() -> argparse.ArgumentParser:
        default=100.0,
        help="Reference limit for TPOT plots (ms)",
    )
-    return parser

+    args = parser.parse_args()

-def choose_metrics(latency: str) -> MetricPlan:
-    latency = (latency or "").lower()
    drop_column = "P99"
+    name_column = "Test name"
+    info_cols = [
+        "Model",
+        "Dataset Name",
+        "Input Len",
+        "Output Len",
+        "TP Size",
+        "PP Size",
+        "# of max concurrency.",
+        "qps",
+    ]

-    if "median" in latency:
-        return MetricPlan(
-            data_cols=["Output Tput (tok/s)", "Median TTFT (ms)", "Median"],
-            drop_column=drop_column,
-        )
-
-    return MetricPlan(
-        data_cols=["Output Tput (tok/s)", "P99 TTFT (ms)", "P99"],
-        drop_column=drop_column,
-    )
-
-
-def prepare_input_files(args, info_cols: list[str]) -> tuple[list[str], list[str]]:
-    if not args.file:
-        raise ValueError("No input files provided. Use -f/--file.")
+    if "median" in args.latency:
+        data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
+        html_msgs_for_data_cols = [
+            "Compare Output Tokens /n",
+            "Median TTFT /n",
+            "Median TPOT /n",
+        ]
+        drop_column = "P99"
+    elif "p99" in args.latency:
+        data_cols_to_compare = ["Output Tput (tok/s)", "P99 TTFT (ms)", "P99"]
+        html_msgs_for_data_cols = [
+            "Compare Output Tokens /n",
+            "P99 TTFT /n",
+            "P99 TPOT /n",
+        ]

    if len(args.file) == 1:
        files = split_json_by_tp_pp(args.file[0], output_root="splits")
        info_cols = [c for c in info_cols if c not in ("TP Size", "PP Size")]
    else:
        files = args.file
-
-    return files, info_cols
-
-
-def get_y_axis_col(info_cols: list[str], xaxis: str) -> str:
-    y_axis_index = info_cols.index(xaxis) if xaxis in info_cols else 6
-    return info_cols[y_axis_index]
-
-
-def get_group_cols(output_df: pd.DataFrame, info_cols: list[str]) -> list[str]:
-    filtered_info_cols = info_cols[:4]
-    group_cols = [c for c in filtered_info_cols if c in output_df.columns]
-    if not group_cols:
-        raise ValueError(
-            f"No valid group-by columns. Expected subset: {filtered_info_cols}, "
-            f"but DataFrame has: {list(output_df.columns)}"
-        )
-    return group_cols
-
-
-def normalize_group_key(name):
-    return name if isinstance(name, tuple) else (name,)
-
-
-def group_filename(name, prefix: str = "perf_comparison_") -> str:
-    name_vals = normalize_group_key(name)
-    safe = ",".join(map(str, name_vals)).replace(",", "_").replace("/", "-")
-    return f"{prefix}{safe}.html"
-
-
-def build_group_suffix(group_cols: list[str], name) -> str:
-    name_vals = normalize_group_key(name)
-    return " , ".join(f"{col} : [ {val} ] " for col, val in zip(group_cols, name_vals))
-
-
-def render_metric_table_html(
-    display_group: pd.DataFrame,
-    metric_label: str,
-    group_suffix: str,
-    args,
-) -> str:
-    title = (
-        f'<div style="font-size: 1.25em; font-weight: 600; margin: 12px 0;">'
-        f"{_html.escape(metric_label)}"
-        f" — {_html.escape(group_suffix)}"
-        f"</div>\n"
-    )
-
-    metric_name = metric_label.lower()
-    if "ttft" in metric_name:
-        styler = _highlight_threshold(display_group, args.ttft_max_ms)
-    elif ("tpot" in metric_name) or ("median" in metric_name) or ("p99" in metric_name):
-        styler = _highlight_threshold(display_group, args.tpot_max_ms)
-    else:
-        styler = display_group.style
-
-    styler = _apply_two_decimals(styler)
-    styler = highlight_ratio_columns(styler)
-
-    return title + styler.to_html(table_attributes='border="1" class="dataframe"')
-
-
-def maybe_write_plot(
-    main_fh,
-    sub_fh,
-    group_df: pd.DataFrame,
-    raw_data_cols: list[str],
-    metric_label: str,
-    y_axis_col: str,
-    args,
-):
-    if not (args.plot and plotly_found):
-        return
-
-    import plotly.express as px
-
-    df = group_df[raw_data_cols].sort_values(by=y_axis_col)
-    df_melted = df.melt(
-        id_vars=y_axis_col,
-        var_name="Configuration",
-        value_name=metric_label,
-    )
-
-    fig = px.line(
-        df_melted,
-        x=y_axis_col,
-        y=metric_label,
-        color="Configuration",
-        title=f"{metric_label} vs {y_axis_col}",
-        markers=True,
-    )
-
-    # Ensure plot hover + y tick labels are also 2 decimals.
-    fig.update_traces(hovertemplate="%{y:.2f}<extra></extra>")
-    fig.update_yaxes(tickformat=".2f")
-
-    metric_name = metric_label.lower()
-    if "ttft" in metric_name:
-        _add_limit_line(fig, args.ttft_max_ms, "TTFT limit")
-    elif ("tpot" in metric_name) or ("median" in metric_name) or ("p99" in metric_name):
-        _add_limit_line(fig, args.tpot_max_ms, "TPOT limit")
-
-    html = fig.to_html(full_html=True, include_plotlyjs="cdn")
-    main_fh.write(html)
-    sub_fh.write(html)
-
-
-def build_group_keys(
-    df: pd.DataFrame, group_cols: list[str], sort_cols: list[str] | None = None
-):
-    if sort_cols:
-        df = df.sort_values(by=sort_cols)
-    gb = df.groupby(group_cols, dropna=False)
-    return [k for k, _ in gb]
-
-
-def write_report_group_first(
-    files: list[str], info_cols: list[str], plan: MetricPlan, args
-):
-    name_column = "Test name"
-    y_axis_col = get_y_axis_col(info_cols, args.xaxis)
-
    print("comparing : " + ", ".join(files))
-
-    metric_cache: dict[str, tuple[pd.DataFrame, list[str]]] = {}
-    group_cols_canonical: list[str] | None = None
-
-    for metric_label in plan.data_cols:
-        output_df, raw_data_cols = compare_data_columns(
-            files,
-            name_column,
-            metric_label,
-            info_cols,
-            plan.drop_column,
-            debug=args.debug,
-        )
-
-        raw_data_cols = list(raw_data_cols)
-        raw_data_cols.insert(0, y_axis_col)
-
-        group_cols = get_group_cols(output_df, info_cols)
-        if group_cols_canonical is None:
-            group_cols_canonical = group_cols
-        else:
-            group_cols_canonical = [c for c in group_cols_canonical if c in group_cols]
-
-        metric_cache[metric_label] = (
-            output_df.sort_values(by=args.xaxis),
-            raw_data_cols,
-        )
-
-    if not group_cols_canonical:
-        raise ValueError("No canonical group columns found across metrics.")
-
-    first_metric = plan.data_cols[0]
-    first_df_sorted, _ = metric_cache[first_metric]
-    group_keys = build_group_keys(
-        first_df_sorted, group_cols_canonical, sort_cols=[args.xaxis]
-    )
-
-    metric_groupbys = {
-        metric_label: df.groupby(group_cols_canonical, dropna=False)
-        for metric_label, (df, _) in metric_cache.items()
-    }
-
-    with open("perf_comparison.html", "w", encoding="utf-8") as main_fh:
-        main_fh.write('<meta charset="utf-8">\n')
-        for gkey in group_keys:
-            gkey_tuple = normalize_group_key(gkey)
-            suffix = build_group_suffix(group_cols_canonical, gkey_tuple)
-            sub_path = group_filename(gkey_tuple)
-            group_header = (
-                '<div style="font-size: 1.4em; font-weight: 700; '
-                'margin: 18px 0 10px 0;">'
-                f"{_html.escape(suffix)}"
-                "</div>\n"
+    debug = args.debug
+    plot = args.plot
+    # For Plot feature, assign y axis from one of info_cols
+    y_axis_index = info_cols.index(args.xaxis) if args.xaxis in info_cols else 6
+    with open("perf_comparison.html", "w") as text_file:
+        for i in range(len(data_cols_to_compare)):
+            output_df, raw_data_cols = compare_data_columns(
+                files,
+                name_column,
+                data_cols_to_compare[i],
+                info_cols,
+                drop_column,
+                debug=debug,
            )

-            main_fh.write(group_header)
-            with open(sub_path, "w", encoding="utf-8") as sub_fh:
-                sub_fh.write('<meta charset="utf-8">\n')
-                sub_fh.write(group_header)
-                tput_group_df = None
-                ttft_group_df = None
-                tpot_group_df = None
-                conc_col = args.xaxis
+            # For Plot feature, insert y axis from one of info_cols
+            raw_data_cols.insert(0, info_cols[y_axis_index])

-                for metric_label in plan.data_cols:
-                    gb = metric_groupbys[metric_label]
-                    df_sorted, raw_data_cols = metric_cache[metric_label]
+            filtered_info_cols = info_cols[:-2]
+            existing_group_cols = [
+                c for c in filtered_info_cols if c in output_df.columns
+            ]
+            if not existing_group_cols:
+                raise ValueError(
+                    f"No valid group-by columns  "
+                    f"Expected subset: {filtered_info_cols}, "
+                    f"but DataFrame has: {list(output_df.columns)}"
+                )
+            # output_df_sorted = output_df.sort_values(by=existing_group_cols)
+            output_df_sorted = output_df.sort_values(by=args.xaxis)
+            output_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
+            for name, group in output_groups:
+                group_name = (
+                    ",".join(map(str, name)).replace(",", "_").replace("/", "-")
+                )
+                group_html_name = "perf_comparison_" + group_name + ".html"

-                    try:
-                        group_df = gb.get_group(gkey)
-                    except KeyError:
-                        missing = (
-                            '<div style="font-size: 1.1em; font-weight: 600; '
-                            'margin: 10px 0;">'
-                            f"{_html.escape(metric_label)} — missing for this group"
-                            "</div>\n"
+                metric_name = str(data_cols_to_compare[i]).lower()
+                if "tok/s" in metric_name:
+                    html = group.to_html()
+                elif "ttft" in metric_name:
+                    styler = _highlight_threshold(group, args.ttft_max_ms).format(
+                        {c: "{:.2f}" for c in group.select_dtypes("number").columns},
+                        na_rep="—",
+                    )
+                    html = styler.to_html(
+                        table_attributes='border="1" class="dataframe"'
+                    )
+                elif (
+                    "tpot" in metric_name
+                    or "median" in metric_name
+                    or "p99" in metric_name
+                ):
+                    styler = _highlight_threshold(group, args.tpot_max_ms).format(
+                        {c: "{:.2f}" for c in group.select_dtypes("number").columns},
+                        na_rep="—",
+                    )
+                    html = styler.to_html(
+                        table_attributes='border="1" class="dataframe"'
+                    )
+
+                text_file.write(html_msgs_for_data_cols[i])
+                text_file.write(html)
+                with open(group_html_name, "a+") as sub_text_file:
+                    sub_text_file.write(html_msgs_for_data_cols[i])
+                    sub_text_file.write(html)
+
+                    if plot and plotly_found:
+                        import plotly.express as px
+
+                        df = group[raw_data_cols]
+                        df_sorted = df.sort_values(by=info_cols[y_axis_index])
+                        # Melt DataFrame for plotting
+                        df_melted = df_sorted.melt(
+                            id_vars=info_cols[y_axis_index],
+                            var_name="Configuration",
+                            value_name=data_cols_to_compare[i],
+                        )
+                        title = (
+                            data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
+                        )
+                        # Create Plotly line chart
+                        fig = px.line(
+                            df_melted,
+                            x=info_cols[y_axis_index],
+                            y=data_cols_to_compare[i],
+                            color="Configuration",
+                            title=title,
+                            markers=True,
                        )

-                        main_fh.write(missing)
-                        sub_fh.write(missing)
-                        continue
+                        # ---- Add threshold lines based on metric name ----
+                        if "ttft" in metric_name:
+                            _add_limit_line(fig, args.ttft_max_ms, "TTFT limit")
+                        elif (
+                            "tpot" in metric_name
+                            or "median" in metric_name
+                            or "p99" in metric_name
+                        ):
+                            _add_limit_line(fig, args.tpot_max_ms, "TPOT limit")

-                    if conc_col not in group_df.columns:
-                        conc_col = _find_concurrency_col(group_df)
-
-                    mn = metric_label.lower().strip()
-                    if "tok/s" in mn:
-                        tput_group_df = group_df
-                    elif "ttft" in mn:
-                        ttft_group_df = group_df
-                    elif mn in ("p99", "median") or "tpot" in mn:
-                        tpot_group_df = group_df
-
-                    display_group = group_df.drop(
-                        columns=group_cols_canonical, errors="ignore"
-                    )
-
-                    html = render_metric_table_html(
-                        display_group, metric_label, suffix, args
-                    )
-                    main_fh.write(html)
-                    sub_fh.write(html)
-
-                    maybe_write_plot(
-                        main_fh,
-                        sub_fh,
-                        group_df=group_df,
-                        raw_data_cols=raw_data_cols,
-                        metric_label=metric_label,
-                        y_axis_col=y_axis_col,
-                        args=args,
-                    )
-
-                summary_html = build_valid_max_concurrency_summary_html(
-                    tput_group_df=tput_group_df,
-                    ttft_group_df=ttft_group_df,
-                    tpot_group_df=tpot_group_df,
-                    conc_col=conc_col,
-                    args=args,
-                )
-                if summary_html:
-                    main_fh.write(summary_html)
-                    sub_fh.write(summary_html)
-
-
-def main():
-    args = build_parser().parse_args()
-    info_cols = list(DEFAULT_INFO_COLS)
-    plan = choose_metrics(args.latency)
-    files, info_cols = prepare_input_files(args, info_cols)
-    write_report_group_first(files, info_cols, plan, args)
-
-
-if __name__ == "__main__":
-    main()
+                        # Export to HTML
+                        text_file.write(
+                            fig.to_html(full_html=True, include_plotlyjs="cdn")
+                        )
+                        sub_text_file.write(
+                            fig.to_html(full_html=True, include_plotlyjs="cdn")
+                        )
--- a/.buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
+++ b/.buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
@@ -49,11 +49,7 @@ check_cpus() {
    echo "Need at least 1 NUMA to run benchmarking."
    exit 1
  fi
-  if [[ "$(uname -m)" == "aarch64" ]] || [[ "$(uname -m)" == "arm64" ]]; then
-    declare -g gpu_type="arm64-cpu"
-  else
-    declare -g gpu_type="cpu"
-  fi
+  declare -g gpu_type="cpu"
  echo "GPU type is $gpu_type"
 }

@@ -114,8 +110,7 @@ json2envs() {
 wait_for_server() {
  # wait for vllm server to start
  # return 1 if vllm server crashes
-  local timeout_val="1200"
-  timeout "$timeout_val" bash -c '
+  timeout 1200 bash -c '
    until curl -X POST localhost:8000/v1/completions; do
      sleep 1
    done' && return 0 || return 1
@@ -211,8 +206,8 @@ run_latency_tests() {

    # check if there is enough GPU to run the test
    tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
-    if [[ "$ON_CPU" == "1" ]]; then
-      pp=$(echo "$latency_params" | jq -r '.pipeline_parallel_size // 1')
+    if [ "$ON_CPU" == "1" ]; then
+      pp=$(echo "$latency_params" | jq -r '.pipeline_parallel_size')
      world_size=$(($tp*$pp))
      if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
        echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
@@ -280,8 +275,8 @@ run_throughput_tests() {

    # check if there is enough GPU to run the test
    tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
-    if [[ "$ON_CPU" == "1" ]]; then
-      pp=$(echo "$throughput_params" | jq -r '.pipeline_parallel_size // 1')
+    if [ "$ON_CPU" == "1" ]; then
+      pp=$(echo "$throughput_params" | jq -r '.pipeline_parallel_size')
      world_size=$(($tp*$pp))
      if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
        echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
@@ -321,44 +316,12 @@ run_throughput_tests() {
 run_serving_tests() {
  # run serving tests using `vllm bench serve` command
  # $1: a json file specifying serving test cases
-  #
-  # Supported JSON formats:
-  # 1) Plain format: top-level array
-  #    [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
-  #
-  # 2) Default parameters field + plain format tests
-  #    {
-  #      "defaults": { ... },
-  #      "tests": [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
-  #    }

  local serving_test_file
  serving_test_file=$1

  # Iterate over serving tests
-  jq -c '
-    if type == "array" then
-      # Plain format: test cases array
-      .[]
-    elif (type == "object" and has("tests")) then
-      # merge the default parameters into each test cases
-      . as $root
-      | ($root.defaults // {}) as $d
-      | ($root.tests // [])[]
-      # default qps / max_concurrency from defaults if missing
-      | .qps_list = (.qps_list // $d.qps_list)
-      | .max_concurrency_list = (.max_concurrency_list // $d.max_concurrency_list)
-      # merge envs / params: test overrides defaults
-      | .server_environment_variables =
-          (($d.server_environment_variables // {}) + (.server_environment_variables // {}))
-      | .server_parameters =
-          (($d.server_parameters // {}) + (.server_parameters // {}))
-      | .client_parameters =
-          (($d.client_parameters // {}) + (.client_parameters // {}))
-    else
-      error("Unsupported serving test file format: must be array or object with .tests")
-    end
-  ' "$serving_test_file" | while read -r params; do
+  jq -c '.[]' "$serving_test_file" | while read -r params; do
    # get the test name, and append the GPU type back to it.
    test_name=$(echo "$params" | jq -r '.test_name')
    if [[ ! "$test_name" =~ ^serving_ ]]; then
@@ -372,33 +335,28 @@ run_serving_tests() {
      continue
    fi

-    # get client and server arguments (after merged the default parameters)
+    # get client and server arguments
    server_params=$(echo "$params" | jq -r '.server_parameters')
    server_envs=$(echo "$params" | jq -r '.server_environment_variables')
    client_params=$(echo "$params" | jq -r '.client_parameters')
-
    server_args=$(json2args "$server_params")
    server_envs=$(json2envs "$server_envs")
    client_args=$(json2args "$client_params")
-
-    # qps_list
    qps_list=$(echo "$params" | jq -r '.qps_list')
    qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
    echo "Running over qps list $qps_list"
-
-    # max_concurrency_list (fallback to num_prompts if missing)
    max_concurrency_list=$(echo "$params" | jq -r '.max_concurrency_list')
    if [[ -z "$max_concurrency_list" || "$max_concurrency_list" == "null" ]]; then
-      num_prompts=$(echo "$client_params" | jq -r '.num_prompts')
-      max_concurrency_list="[$num_prompts]"
+        num_prompts=$(echo "$client_params" | jq -r '.num_prompts')
+        max_concurrency_list="[$num_prompts]"
    fi
    max_concurrency_list=$(echo "$max_concurrency_list" | jq -r '.[] | @sh')
    echo "Running over max concurrency list $max_concurrency_list"

    # check if there is enough resources to run the test
    tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
-    if [[ "$ON_CPU" == "1" ]]; then
-      pp=$(echo "$server_params" | jq -r '.pipeline_parallel_size // 1')
+    if [ "$ON_CPU" == "1" ]; then
+      pp=$(echo "$server_params" | jq -r '.pipeline_parallel_size')
      world_size=$(($tp*$pp))
      if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
        echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
@@ -500,9 +458,9 @@ run_serving_tests() {
 main() {
  local ARCH
  ARCH=''
-  if [[ "$ON_CPU" == "1" ]]; then
-    check_cpus
-    ARCH="-$gpu_type"
+  if [ "$ON_CPU" == "1" ];then
+     check_cpus
+     ARCH='-cpu'
  else
     check_gpus
     ARCH="$arch_suffix"
--- a/.buildkite/performance-benchmarks/tests/latency-tests-arm64-cpu.json
+++ b/.buildkite/performance-benchmarks/tests/latency-tests-arm64-cpu.json
@@ -1,26 +0,0 @@
-[
-    {
-        "test_name": "latency_llama8B_tp1",
-        "environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-            "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-            "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-            "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-            "load_format": "dummy",
-            "dtype": "bfloat16",
-            "distributed_executor_backend": "mp",
-            "block_size": 128,
-            "trust_remote_code": "",
-            "disable_log_stats": "",
-            "enforce_eager": "",
-            "max_num_batched_tokens": 2048,
-            "max_num_seqs": 256,
-            "num_iters_warmup": 5,
-            "num_iters": 15
-        }
-    }
-]
--- a/.buildkite/performance-benchmarks/tests/serving-tests-arm64-cpu.json
+++ b/.buildkite/performance-benchmarks/tests/serving-tests-arm64-cpu.json
@@ -1,130 +0,0 @@
-{
-  "defaults": {
-    "qps_list": [
-      "inf"
-    ],
-    "max_concurrency_list": [
-      12,
-      16,
-      24,
-      32,
-      64,
-      128,
-      200
-    ],
-    "server_environment_variables": {
-      "VLLM_RPC_TIMEOUT": 100000,
-      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-      "VLLM_CPU_SGL_KERNEL": 1,
-      "VLLM_CPU_KVCACHE_SPACE": 40
-    },
-    "server_parameters": {
-      "model": "meta-llama/Llama-3.1-8B-Instruct",
-      "tensor_parallel_size": 1,
-      "dtype": "bfloat16",
-      "distributed_executor_backend": "mp",
-      "block_size": 128,
-      "trust_remote_code": "",
-      "disable_log_stats": "",
-      "enforce_eager": "",
-      "max_num_batched_tokens": 2048,
-      "max_num_seqs": 256,
-      "load_format": "dummy"
-    },
-    "client_parameters": {
-      "model": "meta-llama/Llama-3.1-8B-Instruct",
-      "backend": "vllm",
-      "ignore-eos": "",
-      "num_prompts": 200
-    }
-  },
-  "tests": [
-    {
-      "test_name": "serving_llama8B_tp1_sharegpt",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "sharegpt",
-        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp2_sharegpt",
-      "server_parameters": {
-        "tensor_parallel_size": 2
-      },
-      "client_parameters": {
-        "dataset_name": "sharegpt",
-        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_128_128",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp2_random_128_128",
-      "server_parameters": {
-        "tensor_parallel_size": 2
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_128_2048",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 2048
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp2_random_128_2048",
-      "server_parameters": {
-        "tensor_parallel_size": 2
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 2048
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_2048_128",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 2048,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp2_random_2048_128",
-      "server_parameters": {
-        "tensor_parallel_size": 2
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 2048,
-        "random-output-len": 128
-      }
-    }
-  ]
-}
--- a/.buildkite/performance-benchmarks/tests/serving-tests-cpu-snc2.json
+++ b/.buildkite/performance-benchmarks/tests/serving-tests-cpu-snc2.json
@@ -0,0 +1,610 @@
+[
+    {
+        "test_name": "serving_llama8B_bf16_tp1_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_bf16_tp2_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_bf16_tp4_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 4,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_bf16_tp1_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_bf16_tp2_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_bf16_tp4_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 4,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp1_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
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+            "load_format": "dummy"
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+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp2_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp4_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 4,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp1_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp2_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp4_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 4,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp1_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp2_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
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+	    "VLLM_CPU_KVCACHE_SPACE": 40
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+        "server_parameters": {
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+	    "quantization": "awq",
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+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp4_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
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+	    "quantization": "awq",
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+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
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+	    "max_num_batched_tokens": 2048,
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+            "load_format": "dummy"
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+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
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+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
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+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
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+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp2_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp4_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 4,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    }
+]
--- a/.buildkite/performance-benchmarks/tests/serving-tests-cpu-snc3.json
+++ b/.buildkite/performance-benchmarks/tests/serving-tests-cpu-snc3.json
--- a/.buildkite/performance-benchmarks/tests/serving-tests-cpu.json
+++ b/.buildkite/performance-benchmarks/tests/serving-tests-cpu.json
@@ -1,283 +1,276 @@
-{
-  "defaults": {
-    "qps_list": [
-      "inf"
-    ],
-    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-    "server_environment_variables": {
-      "VLLM_RPC_TIMEOUT": 100000,
-      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-      "VLLM_CPU_SGL_KERNEL": 1,
-      "VLLM_CPU_KVCACHE_SPACE": 40
+[
+    {
+        "test_name": "serving_llama8B_tp1_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [32],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 32
+        }
    },
-    "server_parameters": {
-      "model": "meta-llama/Llama-3.1-8B-Instruct",
-      "tensor_parallel_size": 1,
-      "dtype": "bfloat16",
-      "distributed_executor_backend": "mp",
-      "block_size": 128,
-      "trust_remote_code": "",
-      "disable_log_stats": "",
-      "max_num_batched_tokens": 2048,
-      "max_num_seqs": 256
+    {
+        "test_name": "serving_llama8B_tp2_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [32],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 32
+        }
    },
-    "client_parameters": {
-      "model": "meta-llama/Llama-3.1-8B-Instruct",
-      "backend": "vllm",
-      "ignore-eos": "",
-      "num_prompts": 200
+    {
+        "test_name": "serving_llama8B_tp1_random_128_128",
+        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [32],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 32
+        }
+    },
+    {
+        "test_name": "serving_llama8B_tp2_random_128_128",
+        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [32],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 32
+        }
+    },
+    {
+        "test_name": "serving_llama8B_tp1_random_128_2048",
+        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [32],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 2048,
+	    "ignore-eos": "",
+            "num_prompts": 32
+        }
+    },
+    {
+        "test_name": "serving_llama8B_tp2_random_128_2048",
+        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [32],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 2048,
+	    "ignore-eos": "",
+            "num_prompts": 32
+        }
+    },
+    {
+        "test_name": "serving_llama8B_tp1_random_2048_128",
+        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [32],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 2048,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 32
+        }
+    },
+    {
+        "test_name": "serving_llama8B_tp2_random_2048_128",
+        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [32],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 2048,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 32
+        }
    }
-  },
-  "tests": [
-    {
-      "test_name": "serving_llama8B_tp1_sharegpt",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "sharegpt",
-        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp2_sharegpt",
-      "server_parameters": {
-        "tensor_parallel_size": 2
-      },
-      "client_parameters": {
-        "dataset_name": "sharegpt",
-        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_128_128",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp2_random_128_128",
-      "server_parameters": {
-        "tensor_parallel_size": 2
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp4_random_128_128",
-      "server_parameters": {
-        "tensor_parallel_size": 4
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_128_2048",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 2048
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp2_random_128_2048",
-      "server_parameters": {
-        "tensor_parallel_size": 2
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 2048
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp4_random_128_2048",
-      "server_parameters": {
-        "tensor_parallel_size": 4
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 2048
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_2048_128",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 2048,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp2_random_2048_128",
-      "server_parameters": {
-        "tensor_parallel_size": 2
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 2048,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp4_random_2048_128",
-      "server_parameters": {
-        "tensor_parallel_size": 4
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 2048,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_int4_tp1_random_128_128",
-      "server_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_int4_tp2_random_128_128",
-      "server_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-        "tensor_parallel_size": 2
-      },
-      "client_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_int4_tp4_random_128_128",
-      "server_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-        "tensor_parallel_size": 4
-      },
-      "client_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama3B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_granite2B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "ibm-granite/granite-3.2-2b-instruct",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "ibm-granite/granite-3.2-2b-instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen1.7B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-1.7B",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-1.7B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen4B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-4B",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-4B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen8B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-8B",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-8B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_glm9B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "zai-org/glm-4-9b-hf",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "zai-org/glm-4-9b-hf",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma7B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "google/gemma-7b",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "google/gemma-7b",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    }
-  ]
-}
+]
--- a/.buildkite/performance-benchmarks/tests/throughput-tests-arm64-cpu.json
+++ b/.buildkite/performance-benchmarks/tests/throughput-tests-arm64-cpu.json
@@ -1,27 +0,0 @@
-[
-    {
-        "test_name": "throughput_llama8B_tp1",
-        "environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-            "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-            "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-            "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-            "load_format": "dummy",
-            "dtype": "bfloat16",
-            "distributed_executor_backend": "mp",
-            "block_size": 128,
-            "trust_remote_code": "",
-            "disable_log_stats": "",
-            "enforce_eager": "",
-            "max_num_batched_tokens": 2048,
-            "max_num_seqs": 256,
-            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200,
-            "backend": "vllm"
-        }
-    }
-]
--- a/.buildkite/release-pipeline.yaml
+++ b/.buildkite/release-pipeline.yaml
@@ -1,6 +1,6 @@
 steps:
  # aarch64 + CUDA builds
-  - label: "Build wheel - aarch64 - CUDA 12.9"
+  - label: "Build arm64 wheel - CUDA 12.9"
    depends_on: ~
    id: build-wheel-arm64-cuda-12-9
    agents:
@@ -8,30 +8,15 @@ steps:
    commands:
      # #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
      # https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg VLLM_MAIN_CUDA_VERSION=12.9 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
      - "mkdir artifacts"
      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-nightly-wheels.sh"
-    env:
-      DOCKER_BUILDKIT: "1"
-
-  - label: "Build wheel - aarch64 - CUDA 13.0"
-    depends_on: ~
-    id: build-wheel-arm64-cuda-13-0
-    agents:
-      queue: arm64_cpu_queue_postmerge
-    commands:
-      # #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
-      # https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04  --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
-      - "mkdir artifacts"
-      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
+      - "bash .buildkite/scripts/upload-wheels.sh"
    env:
      DOCKER_BUILDKIT: "1"

  # aarch64 build
-  - label: "Build wheel - aarch64 - CPU"
+  - label: "Build arm64 CPU wheel"
    depends_on: ~
    id: build-wheel-arm64-cpu
    agents:
@@ -40,53 +25,52 @@ steps:
      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_BUILD_ACL=ON --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
      - "mkdir artifacts"
      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
+      - "bash .buildkite/scripts/upload-wheels.sh"
    env:
      DOCKER_BUILDKIT: "1"

  # x86 + CUDA builds
-  - label: "Build wheel - x86_64 - CUDA 12.9"
+  - label: "Build wheel - CUDA 12.8"
    depends_on: ~
-    id: build-wheel-x86-cuda-12-9
+    id: build-wheel-cuda-12-8
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "mkdir artifacts"
+      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
+      - "bash .buildkite/scripts/upload-wheels.sh"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  - label: "Build wheel - CUDA 12.9"
+    depends_on: ~
+    id: build-wheel-cuda-12-9
    agents:
      queue: cpu_queue_postmerge
    commands:
      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
      - "mkdir artifacts"
      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_31"
+      - "bash .buildkite/scripts/upload-wheels.sh"
    env:
      DOCKER_BUILDKIT: "1"

-  - label: "Build wheel - x86_64 - CUDA 13.0"
+  - label: "Build wheel - CUDA 13.0"
    depends_on: ~
-    id: build-wheel-x86-cuda-13-0
+    id: build-wheel-cuda-13-0
    agents:
      queue: cpu_queue_postmerge
    commands:
      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
      - "mkdir artifacts"
      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
+      - "bash .buildkite/scripts/upload-wheels.sh"
    env:
      DOCKER_BUILDKIT: "1"

-  # x86 CPU wheel build
-  - label: "Build wheel - x86_64 - CPU"
-    depends_on: ~
-    id: build-wheel-x86-cpu
-    agents:
-      queue: cpu_queue_postmerge
-    commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --build-arg VLLM_CPU_AMXBF16=true --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
-      - "mkdir artifacts"
-      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
-    env:
-      DOCKER_BUILDKIT: "1"
-
-  # Build release images (CUDA 12.9)
-  - label: "Build release image - x86_64 - CUDA 12.9"
+  # Build release images (12.9)
+  - label: "Build release image (x86)"
    depends_on: ~
    id: build-release-image-x86
    agents:
@@ -99,7 +83,7 @@ steps:
      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"

-  - label: "Build release image - aarch64 - CUDA 12.9"
+  - label: "Build release image (arm64)"
    depends_on: ~
    id: build-release-image-arm64
    agents:
@@ -109,65 +93,28 @@ steps:
      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"

-  - label: "Create multi-arch manifest - CUDA 12.9"
+  # Add job to create multi-arch manifest
+  - label: "Create multi-arch manifest"
    depends_on:
      - build-release-image-x86
      - build-release-image-arm64
    id: create-multi-arch-manifest
    agents:
-      queue: small_cpu_queue_postmerge
+      queue: cpu_queue_postmerge
    commands:
      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
      - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 --amend"
      - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"

-  - label: "Annotate release workflow - CUDA 12.9"
+  - label: "Annotate release workflow"
    depends_on:
      - create-multi-arch-manifest
+      - build-wheel-cuda-12-8
    id: annotate-release-workflow
-    agents:
-      queue: small_cpu_queue_postmerge
-    commands:
-      - "bash .buildkite/scripts/annotate-release.sh"
-
-  - block: "Build CUDA 13.0 release images"
-    key: block-release-image-build-cuda-13-0
-    depends_on: ~
-
-  - label: "Build release image - x86_64 - CUDA 13.0"
-    depends_on: block-release-image-build-cuda-13-0
-    id: build-release-image-x86-cuda-13-0
    agents:
      queue: cpu_queue_postmerge
    commands:
-      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
-      # re-tag to default image tag and push, just in case arm64 build fails
-      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
-
-  - label: "Build release image - aarch64 - CUDA 13.0"
-    depends_on: block-release-image-build-cuda-13-0
-    id: build-release-image-arm64-cuda-13-0
-    agents:
-      queue: arm64_cpu_queue_postmerge
-    commands:
-      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
-
-  - label: "Create multi-arch manifest - CUDA 13.0"
-    depends_on:
-      - build-release-image-x86-cuda-13-0
-      - build-release-image-arm64-cuda-13-0
-    id: create-multi-arch-manifest-cuda-13-0
-    agents:
-      queue: small_cpu_queue_postmerge
-    commands:
-      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu130 --amend"
-      - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
+      - "bash .buildkite/scripts/annotate-release.sh"

  - input: "Provide Release version here"
    id: input-release-version
@@ -175,26 +122,6 @@ steps:
      - text: "What is the release version?"
        key: release-version

-  - block: "Confirm update release wheels to PyPI (experimental, use with caution)?"
-    key: block-upload-release-wheels
-    depends_on:
-      - input-release-version
-      - build-wheel-x86-cuda-12-9
-      - build-wheel-x86-cuda-13-0
-      - build-wheel-x86-cpu
-      - build-wheel-arm64-cuda-12-9
-      - build-wheel-arm64-cuda-13-0
-      - build-wheel-arm64-cpu
-
-  - label: "Upload release wheels to PyPI and GitHub"
-    depends_on:
-      - block-upload-release-wheels
-    id: upload-release-wheels
-    agents:
-      queue: small_cpu_queue_postmerge
-    commands:
-      - "bash .buildkite/scripts/upload-release-wheels.sh"
-
  - block: "Build CPU release image"
    key: block-cpu-release-image-build
    depends_on: ~
@@ -227,30 +154,12 @@ steps:
    env:
      DOCKER_BUILDKIT: "1"

-  - block: "Build ROCm release image"
-    key: block-rocm-release-image-build
-    depends_on: ~
-
-  - label: "Build release image (ROCm)"
-    depends_on: block-rocm-release-image-build
-    id: build-release-image-rocm
-    agents:
-      queue: cpu_queue_postmerge
-    commands:
-      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      # Build base image first
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --tag rocm/vllm-dev:base-$BUILDKITE_COMMIT --target final --progress plain -f docker/Dockerfile.rocm_base ."
-      # Build vLLM ROCm image using the base
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg BASE_IMAGE=rocm/vllm-dev:base-$BUILDKITE_COMMIT --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-rocm --target vllm-openai --progress plain -f docker/Dockerfile.rocm ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-rocm"
-
-  
  - label: "Build and publish nightly multi-arch image to DockerHub"
    depends_on:
      - create-multi-arch-manifest
    if: build.env("NIGHTLY") == "1"
    agents:
-      queue: small_cpu_queue_postmerge
+      queue: cpu_queue_postmerge
    commands:
      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
      - "docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64"
@@ -272,365 +181,3 @@ steps:
    env:
      DOCKER_BUILDKIT: "1"
      DOCKERHUB_USERNAME: "vllmbot"
-
-  # =============================================================================
-  # ROCm Release Pipeline (x86_64 only)
-  # =============================================================================
-  #
-  # vLLM version is determined by the Buildkite checkout (like CUDA pipeline).
-  # To build a specific version, trigger the build from that branch/tag.
-  #
-  # Environment variables for ROCm builds (set via Buildkite UI or schedule):
-  #   ROCM_PYTHON_VERSION: Python version (default: 3.12)
-  #   PYTORCH_ROCM_ARCH: GPU architectures (default: gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151)
-  #   ROCM_UPLOAD_WHEELS: Upload to S3 (default: false for nightly, true for releases)
-  #   ROCM_FORCE_REBUILD: Force rebuild base wheels, ignore S3 cache (default: false)
-  #
-  # Note: ROCm version is determined by BASE_IMAGE in docker/Dockerfile.rocm_base
-  #       (currently rocm/dev-ubuntu-22.04:7.1-complete)
-  #
-  # =============================================================================
-
-  # ROCm Input Step - Collect build configuration (manual trigger only)
-  - input: "ROCm Wheel Release Build Configuration"
-    key: input-rocm-config
-    depends_on: ~
-    if: build.source == "ui"
-    fields:
-      - text: "Python Version"
-        key: "rocm-python-version"
-        default: "3.12"
-        hint: "Python version (e.g., 3.12)"
-      - text: "GPU Architectures"
-        key: "rocm-pytorch-rocm-arch"
-        default: "gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151"
-        hint: "Semicolon-separated GPU architectures"
-      - select: "Upload Wheels to S3"
-        key: "rocm-upload-wheels"
-        default: "true"
-        options:
-          - label: "No - Build only (nightly/dev)"
-            value: "false"
-          - label: "Yes - Upload to S3 (release)"
-            value: "true"
-      - select: "Force Rebuild Base Wheels"
-        key: "rocm-force-rebuild"
-        default: "false"
-        hint: "Ignore S3 cache and rebuild base wheels from scratch"
-        options:
-          - label: "No - Use cached wheels if available"
-            value: "false"
-          - label: "Yes - Rebuild even if cache exists"
-            value: "true"
-
-  # ROCm Job 1: Build ROCm Base Wheels (with S3 caching)
-  - label: ":rocm: Build ROCm Base Wheels"
-    id: build-rocm-base-wheels
-    depends_on:
-      - step: input-rocm-config
-        allow_failure: true  # Allow failure so non-UI builds can proceed (input step is skipped)
-    agents:
-      queue: cpu_queue_postmerge
-    commands:
-      # Set configuration and check cache
-      - |
-        set -euo pipefail
-
-        # Get values from meta-data (set by input step) or use defaults
-        PYTHON_VERSION="$$(buildkite-agent meta-data get rocm-python-version 2>/dev/null || echo '')"
-        export PYTHON_VERSION="$${PYTHON_VERSION:-3.12}"
-
-        PYTORCH_ROCM_ARCH="$$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo '')"
-        export PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH:-gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151}"
-
-        # Check for force rebuild flag
-        ROCM_FORCE_REBUILD="$${ROCM_FORCE_REBUILD:-}"
-        if [ -z "$${ROCM_FORCE_REBUILD}" ]; then
-          ROCM_FORCE_REBUILD="$$(buildkite-agent meta-data get rocm-force-rebuild 2>/dev/null || echo '')"
-        fi
-
-        echo "========================================"
-        echo "ROCm Base Wheels Build Configuration"
-        echo "========================================"
-        echo "  PYTHON_VERSION: $${PYTHON_VERSION}"
-        echo "  PYTORCH_ROCM_ARCH: $${PYTORCH_ROCM_ARCH}"
-        echo "  ROCM_FORCE_REBUILD: $${ROCM_FORCE_REBUILD:-false}"
-        echo "========================================"
-
-        # Save resolved config for later jobs
-        buildkite-agent meta-data set "rocm-python-version" "$${PYTHON_VERSION}"
-        buildkite-agent meta-data set "rocm-pytorch-rocm-arch" "$${PYTORCH_ROCM_ARCH}"
-
-        # Check S3 cache for pre-built wheels
-        CACHE_KEY=$$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
-        CACHE_PATH=$$(.buildkite/scripts/cache-rocm-base-wheels.sh path)
-        echo ""
-        echo "Cache key: $${CACHE_KEY}"
-        echo "Cache path: $${CACHE_PATH}"
-
-        # Save cache key for downstream jobs
-        buildkite-agent meta-data set "rocm-cache-key" "$${CACHE_KEY}"
-
-        CACHE_STATUS="miss"
-        if [ "$${ROCM_FORCE_REBUILD}" != "true" ]; then
-          CACHE_STATUS=$$(.buildkite/scripts/cache-rocm-base-wheels.sh check)
-        else
-          echo "Force rebuild requested, skipping cache check"
-        fi
-
-        if [ "$${CACHE_STATUS}" = "hit" ]; then
-          echo ""
-          echo "CACHE HIT! Downloading pre-built wheels..."
-          echo ""
-          .buildkite/scripts/cache-rocm-base-wheels.sh download
-
-          # Set the S3 path for the cached Docker image (for Job 2 to download)
-          S3_ARTIFACT_PATH="s3://$${S3_BUCKET}/rocm/cache/$${CACHE_KEY}"
-          buildkite-agent meta-data set "rocm-docker-image-s3-path" "$${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
-
-          # Mark that we used cache (for Docker image handling)
-          buildkite-agent meta-data set "rocm-used-cache" "true"
-
-          echo ""
-          echo "Cache download complete. Skipping Docker build."
-          echo "Docker image will be downloaded from: $${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
-        else
-          echo ""
-          echo "CACHE MISS. Building from scratch..."
-          echo ""
-
-          # Build full base image (for later vLLM build)
-          DOCKER_BUILDKIT=1 docker buildx build \
-            --file docker/Dockerfile.rocm_base \
-            --tag rocm/vllm-dev:base-$${BUILDKITE_BUILD_NUMBER} \
-            --build-arg PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH}" \
-            --build-arg PYTHON_VERSION="$${PYTHON_VERSION}" \
-            --build-arg USE_SCCACHE=1 \
-            --build-arg SCCACHE_BUCKET_NAME=vllm-build-sccache \
-            --build-arg SCCACHE_REGION_NAME=us-west-2 \
-            --build-arg SCCACHE_S3_NO_CREDENTIALS=0 \
-            --load \
-            .
-
-          # Build debs_wheel_release stage for wheel extraction
-          DOCKER_BUILDKIT=1 docker buildx build \
-            --file docker/Dockerfile.rocm_base \
-            --tag rocm-base-debs:$${BUILDKITE_BUILD_NUMBER} \
-            --target debs_wheel_release \
-            --build-arg PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH}" \
-            --build-arg PYTHON_VERSION="$${PYTHON_VERSION}" \
-            --build-arg USE_SCCACHE=1 \
-            --build-arg SCCACHE_BUCKET_NAME=vllm-build-sccache \
-            --build-arg SCCACHE_REGION_NAME=us-west-2 \
-            --build-arg SCCACHE_S3_NO_CREDENTIALS=0 \
-            --load \
-            .
-
-          # Extract wheels from Docker image
-          mkdir -p artifacts/rocm-base-wheels
-          container_id=$$(docker create rocm-base-debs:$${BUILDKITE_BUILD_NUMBER})
-          docker cp $${container_id}:/app/debs/. artifacts/rocm-base-wheels/
-          docker rm $${container_id}
-          echo "Extracted base wheels:"
-          ls -lh artifacts/rocm-base-wheels/
-
-          # Upload wheels to S3 cache for future builds
-          echo ""
-          echo "Uploading wheels to S3 cache..."
-          .buildkite/scripts/cache-rocm-base-wheels.sh upload
-
-          # Export base Docker image for reuse in vLLM build
-          mkdir -p artifacts/rocm-docker-image
-          docker save rocm/vllm-dev:base-$${BUILDKITE_BUILD_NUMBER} | gzip > artifacts/rocm-docker-image/rocm-base-image.tar.gz
-          echo "Docker image size:"
-          ls -lh artifacts/rocm-docker-image/
-
-          # Upload large Docker image to S3 (also cached by cache key)
-          S3_ARTIFACT_PATH="s3://$${S3_BUCKET}/rocm/cache/$${CACHE_KEY}"
-          echo "Uploading Docker image to $${S3_ARTIFACT_PATH}/"
-          aws s3 cp artifacts/rocm-docker-image/rocm-base-image.tar.gz "$${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
-
-          # Save the S3 path for downstream jobs
-          buildkite-agent meta-data set "rocm-docker-image-s3-path" "$${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
-
-          # Mark that we did NOT use cache
-          buildkite-agent meta-data set "rocm-used-cache" "false"
-
-          echo ""
-          echo "Build complete. Wheels cached for future builds."
-        fi
-    artifact_paths:
-      - "artifacts/rocm-base-wheels/*.whl"
-    env:
-      DOCKER_BUILDKIT: "1"
-      S3_BUCKET: "vllm-wheels"
-
-  # ROCm Job 2: Build vLLM ROCm Wheel
-  - label: ":python: Build vLLM ROCm Wheel"
-    id: build-rocm-vllm-wheel
-    depends_on:
-      - step: build-rocm-base-wheels
-        allow_failure: false
-    agents:
-      queue: cpu_queue_postmerge
-    timeout_in_minutes: 180
-    commands:
-      # Download artifacts and prepare Docker image
-      - |
-        set -euo pipefail
-
-        # Ensure git tags are up-to-date (Buildkite's default fetch doesn't update tags)
-        # This fixes version detection when tags are moved/force-pushed
-        echo "Fetching latest tags from origin..."
-        git fetch --tags --force origin
-        
-        # Log tag information for debugging version detection
-        echo "========================================"
-        echo "Git Tag Verification"
-        echo "========================================"
-        echo "Current HEAD: $(git rev-parse HEAD)"
-        echo "git describe --tags: $(git describe --tags 2>/dev/null || echo 'No tags found')"
-        echo ""
-        echo "Recent tags (pointing to commits near HEAD):"
-        git tag -l --sort=-creatordate | head -5
-        echo "setuptools_scm version detection:"
-        pip install -q setuptools_scm 2>/dev/null || true
-        python3 -c "import setuptools_scm; print('  Detected version:', setuptools_scm.get_version())" 2>/dev/null || echo "  (setuptools_scm not available in this environment)"
-        echo "========================================"
-
-        # Download wheel artifacts from current build
-        echo "Downloading wheel artifacts from current build"
-        buildkite-agent artifact download "artifacts/rocm-base-wheels/*.whl" .
-
-        # Download Docker image from S3 (too large for Buildkite artifacts)
-        DOCKER_IMAGE_S3_PATH="$$(buildkite-agent meta-data get rocm-docker-image-s3-path 2>/dev/null || echo '')"
-        if [ -z "$${DOCKER_IMAGE_S3_PATH}" ]; then
-          echo "ERROR: rocm-docker-image-s3-path metadata not found"
-          echo "This should have been set by the build-rocm-base-wheels job"
-          exit 1
-        fi
-        echo "Downloading Docker image from $${DOCKER_IMAGE_S3_PATH}"
-        mkdir -p artifacts/rocm-docker-image
-        aws s3 cp "$${DOCKER_IMAGE_S3_PATH}" artifacts/rocm-docker-image/rocm-base-image.tar.gz
-
-        # Load base Docker image and capture the tag
-        echo "Loading base Docker image..."
-        LOAD_OUTPUT=$$(gunzip -c artifacts/rocm-docker-image/rocm-base-image.tar.gz | docker load)
-        echo "$${LOAD_OUTPUT}"
-        # Extract the actual loaded image tag from "Loaded image: <tag>" output
-        # This avoids picking up stale images (like rocm/vllm-dev:nightly) already on the agent
-        BASE_IMAGE_TAG=$$(echo "$${LOAD_OUTPUT}" | grep "Loaded image:" | sed 's/Loaded image: //')
-        if [ -z "$${BASE_IMAGE_TAG}" ]; then
-          echo "ERROR: Failed to extract image tag from docker load output"
-          echo "Load output was: $${LOAD_OUTPUT}"
-          exit 1
-        fi
-        echo "Loaded base image: $${BASE_IMAGE_TAG}"
-
-        # Prepare base wheels for Docker build context
-        mkdir -p docker/context/base-wheels
-        touch docker/context/base-wheels/.keep
-        cp artifacts/rocm-base-wheels/*.whl docker/context/base-wheels/
-        echo "Base wheels for vLLM build:"
-        ls -lh docker/context/base-wheels/
-
-        # Get GPU architectures from meta-data
-        PYTORCH_ROCM_ARCH="$$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo '')"
-        PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH:-gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151}"
-
-        echo "========================================"
-        echo "Building vLLM wheel with:"
-        echo "  BUILDKITE_COMMIT: $${BUILDKITE_COMMIT}"
-        echo "  BUILDKITE_BRANCH: $${BUILDKITE_BRANCH}"
-        echo "  PYTORCH_ROCM_ARCH: $${PYTORCH_ROCM_ARCH}"
-        echo "  BASE_IMAGE: $${BASE_IMAGE_TAG}"
-        echo "========================================"
-
-        # Build vLLM wheel using local checkout (REMOTE_VLLM=0)
-        DOCKER_BUILDKIT=1 docker build \
-          --file docker/Dockerfile.rocm \
-          --target export_vllm_wheel_release \
-          --output type=local,dest=rocm-dist \
-          --build-arg BASE_IMAGE="$${BASE_IMAGE_TAG}" \
-          --build-arg ARG_PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH}" \
-          --build-arg REMOTE_VLLM=0 \
-          --build-arg GIT_REPO_CHECK=1 \
-          --build-arg USE_SCCACHE=1 \
-          --build-arg SCCACHE_BUCKET_NAME=vllm-build-sccache \
-          --build-arg SCCACHE_REGION_NAME=us-west-2 \
-          --build-arg SCCACHE_S3_NO_CREDENTIALS=0 \
-          .
-
-        echo "Built vLLM wheel:"
-        ls -lh rocm-dist/*.whl
-
-        # Copy wheel to artifacts directory
-        mkdir -p artifacts/rocm-vllm-wheel
-        cp rocm-dist/*.whl artifacts/rocm-vllm-wheel/
-        echo "Final vLLM wheel:"
-        ls -lh artifacts/rocm-vllm-wheel/
-    artifact_paths:
-      - "artifacts/rocm-vllm-wheel/*.whl"
-    env:
-      DOCKER_BUILDKIT: "1"
-      S3_BUCKET: "vllm-wheels"
-
-  # ROCm Job 3: Upload Wheels to S3
-  - label: ":s3: Upload ROCm Wheels to S3"
-    id: upload-rocm-wheels
-    depends_on:
-      - step: build-rocm-vllm-wheel
-        allow_failure: false
-    agents:
-      queue: cpu_queue_postmerge
-    timeout_in_minutes: 60
-    commands:
-      # Download all wheel artifacts and run upload
-      - |
-        set -euo pipefail
-
-        # Check if upload is enabled (from env var, meta-data, or release branch)
-        ROCM_UPLOAD_WHEELS="$${ROCM_UPLOAD_WHEELS:-}"
-        if [ -z "$${ROCM_UPLOAD_WHEELS}" ]; then
-          # Try to get from meta-data (input form)
-          ROCM_UPLOAD_WHEELS="$$(buildkite-agent meta-data get rocm-upload-wheels 2>/dev/null || echo '')"
-        fi
-
-        echo "========================================"
-        echo "Upload check:"
-        echo "  ROCM_UPLOAD_WHEELS: $${ROCM_UPLOAD_WHEELS}"
-        echo "  BUILDKITE_BRANCH: $${BUILDKITE_BRANCH}"
-        echo "========================================"
-
-        # Skip upload if not enabled
-        if [ "$${ROCM_UPLOAD_WHEELS}" != "true" ]; then
-          echo "Skipping S3 upload (ROCM_UPLOAD_WHEELS != true, NIGHTLY != 1, not a release branch)"
-          echo "To enable upload, set 'Upload Wheels to S3' to 'Yes' in the build configuration"
-          exit 0
-        fi
-
-        echo "Upload enabled, proceeding..."
-
-        # Download artifacts from current build
-        echo "Downloading artifacts from current build"
-        buildkite-agent artifact download "artifacts/rocm-base-wheels/*.whl" .
-        buildkite-agent artifact download "artifacts/rocm-vllm-wheel/*.whl" .
-
-        # Run upload script
-        bash .buildkite/scripts/upload-rocm-wheels.sh
-    env:
-      DOCKER_BUILDKIT: "1"
-      S3_BUCKET: "vllm-wheels"
-
-  # ROCm Job 4: Annotate ROCm Wheel Release
-  - label: ":memo: Annotate ROCm wheel release"
-    id: annotate-rocm-release
-    depends_on:
-      - step: upload-rocm-wheels
-        allow_failure: true
-    agents:
-      queue: cpu_queue_postmerge
-    commands:
-      - "bash .buildkite/scripts/annotate-rocm-release.sh"
-    env:
-      S3_BUCKET: "vllm-wheels"
--- a/.buildkite/scripts/annotate-release.sh
+++ b/.buildkite/scripts/annotate-release.sh
@@ -23,8 +23,8 @@ To download the wheel (by version):
 aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux1_x86_64.whl .
 aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux2014_aarch64.whl .

+aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu126/vllm-${RELEASE_VERSION}+cu126-cp38-abi3-manylinux1_x86_64.whl .
 aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu129/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu130/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux1_x86_64.whl .
 \`\`\`

 To download and upload the image:
@@ -32,7 +32,6 @@ To download and upload the image:
 \`\`\`
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm

 docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64 vllm/vllm-openai:x86_64
 docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:latest-x86_64
@@ -46,16 +45,9 @@ docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
 docker push vllm/vllm-openai:latest-aarch64
 docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64

-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai:rocm
-docker tag vllm/vllm-openai:rocm vllm/vllm-openai:latest-rocm
-docker tag vllm/vllm-openai:rocm vllm/vllm-openai:v${RELEASE_VERSION}-rocm
-docker push vllm/vllm-openai:latest-rocm
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-rocm
-
-docker manifest rm vllm/vllm-openai:latest
-docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64 --amend
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64 --amend
 docker manifest push vllm/vllm-openai:latest
 docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
 \`\`\`
-EOF 
+EOF 
--- a/.buildkite/scripts/annotate-rocm-release.sh
+++ b/.buildkite/scripts/annotate-rocm-release.sh
@@ -1,74 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Generate Buildkite annotation for ROCm wheel release
-
-set -ex
-
-# Get build configuration from meta-data
-# Extract ROCm version dynamically from Dockerfile.rocm_base
-# BASE_IMAGE format: rocm/dev-ubuntu-22.04:7.1-complete -> extracts "7.1"
-ROCM_VERSION=$(grep -E '^ARG BASE_IMAGE=' docker/Dockerfile.rocm_base | sed -E 's/.*:([0-9]+\.[0-9]+).*/\1/' || echo "unknown")
-PYTHON_VERSION=$(buildkite-agent meta-data get rocm-python-version 2>/dev/null || echo "3.12")
-PYTORCH_ROCM_ARCH=$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo "gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151")
-
-# S3 URLs
-S3_BUCKET="${S3_BUCKET:-vllm-wheels}"
-S3_REGION="${AWS_DEFAULT_REGION:-us-west-2}"
-S3_URL="https://${S3_BUCKET}.s3.${S3_REGION}.amazonaws.com"
-ROCM_PATH="rocm/${BUILDKITE_COMMIT}"
-
-buildkite-agent annotate --style 'success' --context 'rocm-release-workflow' << EOF
-## :rocm: ROCm Wheel Release
-
-### Build Configuration
-| Setting | Value |
-|---------|-------|
-| **ROCm Version** | ${ROCM_VERSION} |
-| **Python Version** | ${PYTHON_VERSION} |
-| **GPU Architectures** | ${PYTORCH_ROCM_ARCH} |
-| **Branch** | \`${BUILDKITE_BRANCH}\` |
-| **Commit** | \`${BUILDKITE_COMMIT}\` |
-
-### :package: Installation
-
-**Install from this build (by commit):**
-\`\`\`bash
-uv pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/{rocm_variant}/
-
-# Example:
-uv pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/rocm700/
-\`\`\`
-
-**Install from nightly (if published):**
-\`\`\`bash
-uv pip install vllm --extra-index-url ${S3_URL}/rocm/nightly/
-\`\`\`
-
-### :floppy_disk: Download Wheels Directly
-
-\`\`\`bash
-# List all ROCm wheels
-aws s3 ls s3://${S3_BUCKET}/${ROCM_PATH}/
-
-# Download specific wheels
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/vllm-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/torch-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/triton_rocm-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/torchvision-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/amdsmi-*.whl .
-\`\`\`
-
-### :gear: Included Packages
- **vllm**: vLLM with ROCm support
- **torch**: PyTorch built for ROCm ${ROCM_VERSION}
- **triton_rocm**: Triton built for ROCm
- **torchvision**: TorchVision for ROCm PyTorch
- **amdsmi**: AMD SMI Python bindings
-
-### :warning: Notes
- These wheels are built for **ROCm ${ROCM_VERSION}** and will NOT work with CUDA GPUs
- Supported GPU architectures: ${PYTORCH_ROCM_ARCH}
- Platform: Linux x86_64 only
-EOF
--- a/.buildkite/scripts/cache-rocm-base-wheels.sh
+++ b/.buildkite/scripts/cache-rocm-base-wheels.sh
@@ -1,140 +0,0 @@
-#!/usr/bin/env bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Cache helper for ROCm base wheels
-#
-# This script manages caching of pre-built ROCm base wheels (torch, triton, etc.)
-# to avoid rebuilding them when Dockerfile.rocm_base hasn't changed.
-#
-# Usage:
-#   cache-rocm-base-wheels.sh check    - Check if cache exists, outputs "hit" or "miss"
-#   cache-rocm-base-wheels.sh upload   - Upload wheels to cache
-#   cache-rocm-base-wheels.sh download - Download wheels from cache
-#   cache-rocm-base-wheels.sh key      - Output the cache key
-#
-# Environment variables:
-#   S3_BUCKET          - S3 bucket name (default: vllm-wheels)
-#   PYTHON_VERSION     - Python version (affects cache key)
-#   PYTORCH_ROCM_ARCH  - GPU architectures (affects cache key)
-#
-# Note: ROCm version is determined by BASE_IMAGE in Dockerfile.rocm_base,
-#       so changes to ROCm version are captured by the Dockerfile hash.
-
-set -euo pipefail
-
-BUCKET="${S3_BUCKET:-vllm-wheels}"
-DOCKERFILE="docker/Dockerfile.rocm_base"
-CACHE_PREFIX="rocm/cache"
-
-# Generate hash from Dockerfile content + build args
-generate_cache_key() {
-    # Include Dockerfile content
-    if [[ ! -f "$DOCKERFILE" ]]; then
-        echo "ERROR: Dockerfile not found: $DOCKERFILE" >&2
-        exit 1
-    fi
-    local dockerfile_hash=$(sha256sum "$DOCKERFILE" | cut -c1-16)
-
-    # Include key build args that affect the output
-    # These should match the ARGs in Dockerfile.rocm_base that change the build output
-    # Note: ROCm version is determined by BASE_IMAGE in the Dockerfile, so it's captured by dockerfile_hash
-    local args_string="${PYTHON_VERSION:-}|${PYTORCH_ROCM_ARCH:-}"
-    local args_hash=$(echo "$args_string" | sha256sum | cut -c1-8)
-
-    echo "${dockerfile_hash}-${args_hash}"
-}
-
-CACHE_KEY=$(generate_cache_key)
-CACHE_PATH="s3://${BUCKET}/${CACHE_PREFIX}/${CACHE_KEY}/"
-
-case "${1:-}" in
-    check)
-        echo "Checking cache for key: ${CACHE_KEY}" >&2
-        echo "Cache path: ${CACHE_PATH}" >&2
-        echo "Variables used in cache key:" >&2
-        echo "  PYTHON_VERSION: ${PYTHON_VERSION:-<not set>}" >&2
-        echo "  PYTORCH_ROCM_ARCH: ${PYTORCH_ROCM_ARCH:-<not set>}" >&2
-
-        # Check if cache exists by listing objects
-        # We look for at least one .whl file
-        echo "Running: aws s3 ls ${CACHE_PATH}" >&2
-        S3_OUTPUT=$(aws s3 ls "${CACHE_PATH}" 2>&1) || true
-        echo "S3 ls output:" >&2
-        echo "$S3_OUTPUT" | head -5 >&2
-
-        if echo "$S3_OUTPUT" | grep -q "\.whl"; then
-            echo "hit"
-        else
-            echo "miss"
-        fi
-        ;;
-
-    upload)
-        echo "========================================"
-        echo "Uploading wheels to cache"
-        echo "========================================"
-        echo "Cache key: ${CACHE_KEY}"
-        echo "Cache path: ${CACHE_PATH}"
-        echo ""
-
-        if [[ ! -d "artifacts/rocm-base-wheels" ]]; then
-            echo "ERROR: artifacts/rocm-base-wheels directory not found" >&2
-            exit 1
-        fi
-
-        WHEEL_COUNT=$(ls artifacts/rocm-base-wheels/*.whl 2>/dev/null | wc -l)
-        if [[ "$WHEEL_COUNT" -eq 0 ]]; then
-            echo "ERROR: No wheels found in artifacts/rocm-base-wheels/" >&2
-            exit 1
-        fi
-
-        echo "Uploading $WHEEL_COUNT wheels..."
-        aws s3 cp --recursive artifacts/rocm-base-wheels/ "${CACHE_PATH}"
-
-        echo ""
-        echo "Cache upload complete!"
-        echo "========================================"
-        ;;
-
-    download)
-        echo "========================================"
-        echo "Downloading wheels from cache"
-        echo "========================================"
-        echo "Cache key: ${CACHE_KEY}"
-        echo "Cache path: ${CACHE_PATH}"
-        echo ""
-
-        mkdir -p artifacts/rocm-base-wheels
-        aws s3 cp --recursive "${CACHE_PATH}" artifacts/rocm-base-wheels/
-
-        echo ""
-        echo "Downloaded wheels:"
-        ls -lh artifacts/rocm-base-wheels/
-
-        WHEEL_COUNT=$(ls artifacts/rocm-base-wheels/*.whl 2>/dev/null | wc -l)
-        echo ""
-        echo "Total: $WHEEL_COUNT wheels"
-        echo "========================================"
-        ;;
-
-    key)
-        echo "${CACHE_KEY}"
-        ;;
-
-    path)
-        echo "${CACHE_PATH}"
-        ;;
-
-    *)
-        echo "Usage: $0 {check|upload|download|key|path}" >&2
-        echo "" >&2
-        echo "Commands:" >&2
-        echo "  check    - Check if cache exists, outputs 'hit' or 'miss'" >&2
-        echo "  upload   - Upload wheels from artifacts/rocm-base-wheels/ to cache" >&2
-        echo "  download - Download wheels from cache to artifacts/rocm-base-wheels/" >&2
-        echo "  key      - Output the cache key" >&2
-        echo "  path     - Output the full S3 cache path" >&2
-        exit 1
-        ;;
-esac
--- a/.buildkite/scripts/generate-nightly-index.py
+++ b/.buildkite/scripts/generate-nightly-index.py
@@ -1,468 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# do not complain about line length (for docstring)
-# ruff: noqa: E501
-
-import argparse
-import json
-import sys
-from dataclasses import asdict, dataclass
-from datetime import datetime
-from pathlib import Path
-from typing import Any
-from urllib.parse import quote
-
-import regex as re
-
-
-def normalize_package_name(name: str) -> str:
-    """
-    Normalize package name according to PEP 503.
-    https://peps.python.org/pep-0503/#normalized-names
-
-    Replace runs of underscores, hyphens, and periods with a single hyphen,
-    and lowercase the result.
-    """
-    return re.sub(r"[-_.]+", "-", name).lower()
-
-
-if not sys.version_info >= (3, 12):
-    raise RuntimeError("This script requires Python 3.12 or higher.")
-
-INDEX_HTML_TEMPLATE = """<!DOCTYPE html>
-<html>
-  <!-- {comment} -->
-  <meta name="pypi:repository-version" content="1.0">
-  <body>
-{items}
-  </body>
-</html>
-"""
-
-
-@dataclass
-class WheelFileInfo:
-    package_name: str
-    version: str
-    build_tag: str | None
-    python_tag: str
-    abi_tag: str
-    platform_tag: str
-    variant: str | None
-    filename: str
-
-
-def parse_from_filename(file: str) -> WheelFileInfo:
-    """
-    Parse wheel file name to extract metadata.
-
-    The format of wheel names:
-        {package_name}-{version}(-{build_tag})?-{python_tag}-{abi_tag}-{platform_tag}.whl
-    All versions could contain a variant like '+cu129' or '.cpu' or `.rocm` (or not).
-    Example:
-        vllm-0.11.0-cp38-abi3-manylinux1_x86_64.whl
-        vllm-0.10.2rc2+cu129-cp38-abi3-manylinux2014_aarch64.whl
-        vllm-0.11.1rc8.dev14+gaa384b3c0-cp38-abi3-manylinux2014_aarch64.whl
-        vllm-0.11.1rc8.dev14+gaa384b3c0.cu130-cp38-abi3-manylinux1_x86_64.whl
-    """
-    wheel_file_re = re.compile(
-        r"^(?P<package_name>.+)-(?P<version>[^-]+?)(-(?P<build_tag>[^-]+))?-(?P<python_tag>[^-]+)-(?P<abi_tag>[^-]+)-(?P<platform_tag>[^-]+)\.whl$"
-    )
-    match = wheel_file_re.match(file)
-    if not match:
-        raise ValueError(f"Invalid wheel file name: {file}")
-
-    package_name = match.group("package_name")
-    version = match.group("version")
-    build_tag = match.group("build_tag")
-    python_tag = match.group("python_tag")
-    abi_tag = match.group("abi_tag")
-    platform_tag = match.group("platform_tag")
-
-    # extract variant from version
-    variant = None
-    if "dev" in version:
-        ver_after_dev = version.split("dev")[-1]
-        if "." in ver_after_dev:
-            variant = ver_after_dev.split(".")[-1]
-            version = version.removesuffix("." + variant)
-    else:
-        if "+" in version:
-            version_part, suffix = version.split("+", 1)
-            # Only treat known patterns as variants (rocmXXX, cuXXX, cpu)
-            # Git hashes and other suffixes are NOT variants
-            if suffix.startswith(("rocm", "cu", "cpu")):
-                variant = suffix
-                version = version_part
-            # Otherwise keep the full version string (variant stays None)
-
-    return WheelFileInfo(
-        package_name=package_name,
-        version=version,
-        build_tag=build_tag,
-        python_tag=python_tag,
-        abi_tag=abi_tag,
-        platform_tag=platform_tag,
-        variant=variant,
-        filename=file,
-    )
-
-
-def generate_project_list(subdir_names: list[str], comment: str = "") -> str:
-    """
-    Generate project list HTML content linking to each project & variant sub-directory.
-    """
-    href_tags = []
-    for name in sorted(subdir_names):
-        name = name.strip("/").strip(".")
-        href_tags.append(f'    <a href="{name}/">{name}/</a><br/>')
-    return INDEX_HTML_TEMPLATE.format(items="\n".join(href_tags), comment=comment)
-
-
-def generate_package_index_and_metadata(
-    wheel_files: list[WheelFileInfo],
-    wheel_base_dir: Path,
-    index_base_dir: Path,
-    comment: str = "",
-) -> tuple[str, str]:
-    """
-    Generate package index HTML content for a specific package, linking to actual wheel files.
-    """
-    href_tags = []
-    metadata = []
-    for file in sorted(wheel_files, key=lambda x: x.filename):
-        relative_path = (
-            wheel_base_dir.relative_to(index_base_dir, walk_up=True) / file.filename
-        )
-        # handle with '+' in URL, and avoid double-encoding '/' and already-encoded '%2B'
-        # NOTE: this is AWS S3 specific behavior!
-        file_path_quoted = quote(relative_path.as_posix(), safe=":%/")
-        href_tags.append(f'    <a href="{file_path_quoted}">{file.filename}</a><br/>')
-        file_meta = asdict(file)
-        file_meta["path"] = file_path_quoted
-        metadata.append(file_meta)
-    index_str = INDEX_HTML_TEMPLATE.format(items="\n".join(href_tags), comment=comment)
-    metadata_str = json.dumps(metadata, indent=2)
-    return index_str, metadata_str
-
-
-def generate_index_and_metadata(
-    whl_files: list[str],
-    wheel_base_dir: Path,
-    index_base_dir: Path,
-    default_variant: str | None = None,
-    alias_to_default: str | None = None,
-    comment: str = "",
-):
-    """
-    Generate index for all wheel files.
-
-    Args:
-        whl_files (list[str]): List of wheel files (must be directly under `wheel_base_dir`).
-        wheel_base_dir (Path): Base directory for wheel files.
-        index_base_dir (Path): Base directory to store index files.
-        default_variant (str | None): The default variant name, if any.
-        alias_to_default (str | None): Alias variant name for the default variant, if any.
-        comment (str | None): Optional comment to include in the generated HTML files.
-
-    First, parse all wheel files to extract metadata.
-    We need to collect all wheel files for each variant, and generate an index for it (in a sub-directory).
-    The index for the default variant (if any) is generated in the root index directory.
-
-    If `default_variant` is provided, all wheels must have variant suffixes, and the default variant index
-    is purely a copy of the corresponding variant index, with only the links adjusted.
-    Otherwise, all wheels without variant suffixes are treated as the default variant.
-
-    If `alias_to_default` is provided, an additional alias sub-directory is created, it has the same content
-    as the default variant index, but the links are adjusted accordingly.
-
-    Index directory structure:
-        index_base_dir/ (hosted at wheels.vllm.ai/{nightly,$commit,$version}/)
-            index.html  # project list, linking to "vllm/" and other packages, and all variant sub-directories
-            vllm/
-                index.html # package index, pointing to actual files in wheel_base_dir (relative path)
-                metadata.json # machine-readable metadata for all wheels in this package
-            cpu/ # cpu variant sub-directory
-                index.html
-                vllm/
-                    index.html
-                    metadata.json
-            cu129/ # cu129 is actually the alias to default variant
-                index.html
-                vllm/
-                    index.html
-                    metadata.json
-            cu130/ # cu130 variant sub-directory
-                index.html
-                vllm/
-                    index.html
-                    metadata.json
-            ...
-
-    metadata.json stores a dump of all wheel files' metadata in a machine-readable format:
-        [
-            {
-                "package_name": "vllm",
-                "version": "0.10.2rc2",
-                "build_tag": null,
-                "python_tag": "cp38",
-                "abi_tag": "abi3",
-                "platform_tag": "manylinux2014_aarch64",
-                "variant": "cu129",
-                "filename": "vllm-0.10.2rc2+cu129-cp38-abi3-manylinux2014_aarch64.whl",
-                "path": "../vllm-0.10.2rc2%2Bcu129-cp38-abi3-manylinux2014_aarch64.whl" # to be concatenated with the directory URL and URL-encoded
-            },
-            ...
-        ]
-    """
-
-    parsed_files = [parse_from_filename(f) for f in whl_files]
-
-    if not parsed_files:
-        print("No wheel files found, skipping index generation.")
-        return
-
-    # For ROCm builds: inherit variant from vllm wheel
-    # All ROCm wheels should share the same variant as vllm
-    rocm_variant = None
-    for file in parsed_files:
-        if (
-            file.package_name == "vllm"
-            and file.variant
-            and file.variant.startswith("rocm")
-        ):
-            rocm_variant = file.variant
-            print(f"Detected ROCm variant from vllm: {rocm_variant}")
-            break
-
-    # Apply ROCm variant to all wheels without a variant
-    if rocm_variant:
-        for file in parsed_files:
-            if file.variant is None:
-                file.variant = rocm_variant
-                print(f"Inherited variant '{rocm_variant}' for {file.filename}")
-
-    # Group by variant
-    variant_to_files: dict[str, list[WheelFileInfo]] = {}
-    for file in parsed_files:
-        variant = file.variant or "default"
-        if variant not in variant_to_files:
-            variant_to_files[variant] = []
-        variant_to_files[variant].append(file)
-
-    print(f"Found variants: {list(variant_to_files.keys())}")
-
-    # sanity check for default variant
-    if default_variant:
-        if "default" in variant_to_files:
-            raise ValueError(
-                "All wheel files must have variant suffixes when `default_variant` is specified."
-            )
-        if default_variant not in variant_to_files:
-            raise ValueError(
-                f"Default variant '{default_variant}' not found among wheel files."
-            )
-
-    if alias_to_default:
-        if "default" not in variant_to_files:
-            # e.g. only some wheels are uploaded to S3 currently
-            print(
-                "[WARN] Alias to default variant specified, but no default variant found."
-            )
-        elif alias_to_default in variant_to_files:
-            raise ValueError(
-                f"Alias variant name '{alias_to_default}' already exists among wheel files."
-            )
-        else:
-            variant_to_files[alias_to_default] = variant_to_files["default"].copy()
-            print(f"Alias variant '{alias_to_default}' created for default variant.")
-
-    # Generate comment in HTML header
-    comment_str = f" ({comment})" if comment else ""
-    comment_tmpl = f"Generated on {datetime.now().isoformat()}{comment_str}"
-
-    # Generate index for each variant
-    subdir_names = set()
-    for variant, files in variant_to_files.items():
-        if variant == "default":
-            variant_dir = index_base_dir
-        else:
-            variant_dir = index_base_dir / variant
-            subdir_names.add(variant)
-
-        variant_dir.mkdir(parents=True, exist_ok=True)
-
-        # gather all package names in this variant (normalized per PEP 503)
-        packages = set(normalize_package_name(f.package_name) for f in files)
-        if variant == "default":
-            # these packages should also appear in the "project list"
-            # generate after all variants are processed
-            subdir_names = subdir_names.union(packages)
-        else:
-            # generate project list for this variant directly
-            project_list_str = generate_project_list(sorted(packages), comment_tmpl)
-            with open(variant_dir / "index.html", "w") as f:
-                f.write(project_list_str)
-
-        for package in packages:
-            # filter files belonging to this package only (compare normalized names)
-            package_files = [
-                f for f in files if normalize_package_name(f.package_name) == package
-            ]
-            package_dir = variant_dir / package
-            package_dir.mkdir(parents=True, exist_ok=True)
-            index_str, metadata_str = generate_package_index_and_metadata(
-                package_files, wheel_base_dir, package_dir, comment
-            )
-            with open(package_dir / "index.html", "w") as f:
-                f.write(index_str)
-            with open(package_dir / "metadata.json", "w") as f:
-                f.write(metadata_str)
-
-    # Generate top-level project list index
-    project_list_str = generate_project_list(sorted(subdir_names), comment_tmpl)
-    with open(index_base_dir / "index.html", "w") as f:
-        f.write(project_list_str)
-
-
-if __name__ == "__main__":
-    """
-    Arguments:
-        --version <version> : version string for the current build (e.g., commit hash)
-        --wheel-dir <wheel_directory> : directory containing wheel files (default to be same as `version`)
-        --current-objects <path_to_json> : path to JSON file containing current S3 objects listing in this version directory
-        --output-dir <output_directory> : directory to store generated index files
-        --alias-to-default <alias_variant_name> : (optional) alias variant name for the default variant
-        --comment <comment_string> : (optional) comment string to include in generated HTML files
-    """
-
-    parser = argparse.ArgumentParser(
-        description="Process nightly build wheel files to generate indices."
-    )
-    parser.add_argument(
-        "--version",
-        type=str,
-        required=True,
-        help="Version string for the current build (e.g., commit hash)",
-    )
-    parser.add_argument(
-        "--current-objects",
-        type=str,
-        required=True,
-        help="Path to JSON file containing current S3 objects listing in this version directory",
-    )
-    parser.add_argument(
-        "--output-dir",
-        type=str,
-        required=True,
-        help="Directory to store generated index files",
-    )
-    parser.add_argument(
-        "--wheel-dir",
-        type=str,
-        default=None,
-        help="Directory containing wheel files (default to be same as `version`)",
-    )
-    parser.add_argument(
-        "--alias-to-default",
-        type=str,
-        default=None,
-        help="Alias variant name for the default variant",
-    )
-    parser.add_argument(
-        "--comment",
-        type=str,
-        default="",
-        help="Optional comment string to include in generated HTML files",
-    )
-
-    args = parser.parse_args()
-
-    version = args.version
-    # Allow rocm/ prefix, reject other slashes and all backslashes
-    if "\\" in version:
-        raise ValueError("Version string must not contain backslashes.")
-    if "/" in version and not version.startswith("rocm/"):
-        raise ValueError(
-            "Version string must not contain slashes (except for 'rocm/' prefix)."
-        )
-    current_objects_path = Path(args.current_objects)
-    output_dir = Path(args.output_dir)
-    if not output_dir.exists():
-        output_dir.mkdir(parents=True, exist_ok=True)
-
-    # Read current objects JSON
-    with open(current_objects_path) as f:
-        current_objects: dict[str, list[dict[str, Any]]] = json.load(f)
-
-    # current_objects looks like from list_objects_v2 S3 API:
-    """
-    "Contents": [
-        {
-            "Key": "e2f56c309d2a28899c68975a7e104502d56deb8f/vllm-0.11.2.dev363+ge2f56c309-cp38-abi3-manylinux1_x86_64.whl",
-            "LastModified": "2025-11-28T14:00:32+00:00",
-            "ETag": "\"37a38339c7cdb61ca737021b968075df-52\"",
-            "ChecksumAlgorithm": [
-                "CRC64NVME"
-            ],
-            "ChecksumType": "FULL_OBJECT",
-            "Size": 435649349,
-            "StorageClass": "STANDARD"
-        },
-        ...
-    ]
-    """
-
-    # Extract wheel file keys
-    wheel_files = []
-    for item in current_objects.get("Contents", []):
-        key: str = item["Key"]
-        if key.endswith(".whl"):
-            wheel_files.append(key.split("/")[-1])  # only the filename is used
-
-    print(f"Found {len(wheel_files)} wheel files for version {version}: {wheel_files}")
-
-    # keep only "official" files for a non-nightly version (specified by cli args)
-    PY_VERSION_RE = re.compile(r"^\d+\.\d+\.\d+([a-zA-Z0-9.+-]*)?$")
-    if PY_VERSION_RE.match(version):
-        # upload-wheels.sh ensures no "dev" is in args.version
-        wheel_files = list(
-            filter(lambda x: version in x and "dev" not in x, wheel_files)
-        )
-        print(f"Non-nightly version detected, wheel files used: {wheel_files}")
-    else:
-        print("Nightly version detected, keeping all wheel files.")
-
-    # Generate index and metadata, assuming wheels and indices are stored as:
-    # s3://vllm-wheels/{wheel_dir}/<wheel files>
-    # s3://vllm-wheels/<anything>/<index files>
-    #
-    # For ROCm builds, version is "rocm/{commit}" and indices are uploaded to:
-    #   - rocm/{commit}/  (same as wheels)
-    #   - rocm/nightly/
-    #   - rocm/{version}/
-    # All these are under the "rocm/" prefix, so relative paths should be
-    # relative to "rocm/", not the bucket root.
-    if args.wheel_dir:
-        # Explicit wheel-dir provided (e.g., for version-specific indices pointing to commit dir)
-        wheel_dir = args.wheel_dir.strip().rstrip("/")
-    elif version.startswith("rocm/"):
-        # For rocm/commit, wheel_base_dir should be just the commit part
-        # so relative path from rocm/0.12.0/rocm710/vllm/ -> ../../../{commit}/
-        wheel_dir = version.split("/", 1)[1]
-    else:
-        wheel_dir = version
-    wheel_base_dir = Path(output_dir).parent / wheel_dir
-    index_base_dir = Path(output_dir)
-
-    generate_index_and_metadata(
-        whl_files=wheel_files,
-        wheel_base_dir=wheel_base_dir,
-        index_base_dir=index_base_dir,
-        default_variant=None,
-        alias_to_default=args.alias_to_default,
-        comment=args.comment.strip(),
-    )
-    print(f"Successfully generated index and metadata in {output_dir}")
--- a/.buildkite/scripts/hardware_ci/run-amd-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-amd-test.sh
@@ -141,6 +141,7 @@ if [[ $commands == *" entrypoints/openai "* ]]; then
  --ignore=entrypoints/openai/test_audio.py \
  --ignore=entrypoints/openai/test_shutdown.py \
  --ignore=entrypoints/openai/test_completion.py \
+  --ignore=entrypoints/openai/test_sleep.py \
  --ignore=entrypoints/openai/test_models.py \
  --ignore=entrypoints/openai/test_lora_adapters.py \
  --ignore=entrypoints/openai/test_return_tokens_as_ids.py \
@@ -209,21 +210,12 @@ if [[ $commands == *"--shard-id="* ]]; then
    wait "${pid}"
    STATUS+=($?)
  done
-  at_least_one_shard_with_tests=0
  for st in "${STATUS[@]}"; do
-    if [[ ${st} -ne 0 ]] && [[ ${st} -ne 5 ]]; then
+    if [[ ${st} -ne 0 ]]; then
      echo "One of the processes failed with $st"
      exit "${st}"
-    elif [[ ${st} -eq 5 ]]; then
-      echo "Shard exited with status 5 (no tests collected) - treating as success"
-    else # This means st is 0
-      at_least_one_shard_with_tests=1
    fi
  done
-  if [[ ${#STATUS[@]} -gt 0 && ${at_least_one_shard_with_tests} -eq 0 ]]; then
-    echo "All shards reported no tests collected. Failing the build."
-    exit 1
-  fi
 else
  echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
  docker run \
--- a/.buildkite/scripts/hardware_ci/run-cpu-test-arm.sh
+++ b/.buildkite/scripts/hardware_ci/run-cpu-test-arm.sh
@@ -1,68 +0,0 @@
-#!/bin/bash
-
-# This script build the CPU docker image and run the offline inference inside the container.
-# It serves a sanity check for compilation and basic model usage.
-set -ex
-
-# allow to bind to different cores
-CORE_RANGE=${CORE_RANGE:-0-16}
-OMP_CORE_RANGE=${OMP_CORE_RANGE:-0-16}
-
-export CMAKE_BUILD_PARALLEL_LEVEL=16
-
-# Setup cleanup
-remove_docker_container() {
-    set -e;
-    docker rm -f cpu-test || true;
-}
-trap remove_docker_container EXIT
-remove_docker_container
-
-# Try building the docker image
-docker build --tag cpu-test --target vllm-test -f docker/Dockerfile.cpu .
-
-# Run the image
-docker run -itd --cpuset-cpus="$CORE_RANGE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=16 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test cpu-test
-
-function cpu_tests() {
-  set -e
-
-  docker exec cpu-test bash -c "
-    set -e
-    pip list"
-
-  # offline inference
-  docker exec cpu-test bash -c "
-    set -e
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
-
-  # Run model tests
-  docker exec cpu-test bash -c "
-    set -e
-    pytest -x -v -s tests/models/multimodal/generation/test_whisper.py -m cpu_model"
-
-  # Run kernel tests
-  docker exec cpu-test bash -c "
-    set -e
-    pytest -x -v -s tests/kernels/test_onednn.py
-    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
-    pytest -x -v -s tests/kernels/moe/test_moe.py -k test_cpu_fused_moe_basic"
-
-  # basic online serving
-  docker exec cpu-test bash -c '
-    set -e
-    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS vllm serve Qwen/Qwen3-0.6B --max-model-len 2048 &
-    server_pid=$!
-    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
-    vllm bench serve \
-      --backend vllm \
-      --dataset-name random \
-      --model Qwen/Qwen3-0.6B \
-      --num-prompts 20 \
-      --endpoint /v1/completions
-    kill -s SIGTERM $server_pid &'
-}
-
-# All of CPU tests are expected to be finished less than 40 mins.
-export -f cpu_tests
-timeout 2h bash -c cpu_tests
--- a/.buildkite/scripts/hardware_ci/run-cpu-test-ppc64le.sh
+++ b/.buildkite/scripts/hardware_ci/run-cpu-test-ppc64le.sh
@@ -25,22 +25,20 @@ function cpu_tests() {

  # offline inference
  podman exec -it "$container_id" bash -c "
-    export TORCH_COMPILE_DISABLE=1
    set -xve
    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m" >> $HOME/test_basic.log

  # Run basic model test
  podman exec -it "$container_id" bash -c "
-    export TORCH_COMPILE_DISABLE=1
    set -evx
    pip install pytest pytest-asyncio einops peft Pillow soundfile transformers_stream_generator matplotlib
-    pip install sentence-transformers datamodel_code_generator tblib 
+    pip install sentence-transformers datamodel_code_generator

    # Note: disable Bart until supports V1
    # pytest -v -s tests/models/language/generation/test_bart.py -m cpu_model
-    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-False-5-32-openai-community/gpt2]
-    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-False-5-32-facebook/opt-125m]
-    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-False-5-32-google/gemma-1.1-2b-it]
+    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-openai-community/gpt2]
+    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-facebook/opt-125m]
+    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-google/gemma-1.1-2b-it]
    pytest -v -s tests/models/language/pooling/test_classification.py::test_models[float-jason9693/Qwen2.5-1.5B-apeach]
    # TODO: Below test case tests/models/language/pooling/test_embedding.py::test_models[True-ssmits/Qwen2-7B-Instruct-embed-base] fails on ppc64le. Disabling it for time being.
    # pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model" >> $HOME/test_rest.log
--- a/.buildkite/scripts/hardware_ci/run-cpu-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-cpu-test.sh
@@ -21,8 +21,8 @@ trap remove_docker_container EXIT
 remove_docker_container

 # Try building the docker image
-numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --progress plain --tag cpu-test-"$NUMA_NODE" --target vllm-test -f docker/Dockerfile.cpu .
-numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --progress plain --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$NUMA_NODE"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$NUMA_NODE" --target vllm-test -f docker/Dockerfile.cpu .
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$NUMA_NODE"-avx2 --target vllm-test -f docker/Dockerfile.cpu .

 # Run the image, setting --shm-size=4g for tensor parallel.
 docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=16 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
@@ -50,7 +50,6 @@ function cpu_tests() {
  docker exec cpu-test-"$NUMA_NODE" bash -c "
    set -e
    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
-    pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
    pytest -x -v -s tests/kernels/test_onednn.py"

  # Run basic model test
@@ -74,17 +73,18 @@ function cpu_tests() {
    pytest -x -s -v \
    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"

-  # Run AWQ/GPTQ test
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
-    set -e
-    pytest -x -s -v \
-    tests/quantization/test_cpu_wna16.py"
+  # Note: disable it until supports V1
+  # Run AWQ test
+  # docker exec cpu-test-"$NUMA_NODE" bash -c "
+  #   set -e
+  #   pytest -x -s -v \
+  #   tests/quantization/test_ipex_quant.py"

  # Run multi-lora tests
  docker exec cpu-test-"$NUMA_NODE" bash -c "
    set -e
    pytest -x -s -v \
-    tests/lora/test_qwenvl.py"
+    tests/lora/test_qwen2vl.py"

  # online serving: tp+pp
  docker exec cpu-test-"$NUMA_NODE" bash -c '
--- a/.buildkite/scripts/hardware_ci/run-npu-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-npu-test.sh
@@ -74,7 +74,6 @@ FROM ${BASE_IMAGE_NAME}

 # Define environments
 ENV DEBIAN_FRONTEND=noninteractive
-ENV SOC_VERSION="ascend910b1"

 RUN pip config set global.index-url http://cache-service-vllm.nginx-pypi-cache.svc.cluster.local:${PYPI_CACHE_PORT}/pypi/simple && \
    pip config set global.trusted-host cache-service-vllm.nginx-pypi-cache.svc.cluster.local && \
--- a/.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh
+++ b/.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh
@@ -61,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.9.2" \
+    && python3 -m pip install --progress-bar off "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
    && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"

--- a/.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh
@@ -61,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.9.2" \
+    && python3 -m pip install --progress-bar off "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
    && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"

--- a/.buildkite/scripts/hardware_ci/run-xpu-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-xpu-test.sh
@@ -35,11 +35,10 @@ docker run \
    echo $ZE_AFFINITY_MASK
    pip install tblib==3.1.0
    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 -O3 -cc.cudagraph_mode=NONE
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 -O3 -O.cudagraph_mode=NONE
    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
-    python3 examples/offline_inference/basic/generate.py --model Intel/Qwen2.5-0.5B-W4A16-G128-AutoRound-LLMC-TEST-ONLY --enforce-eager
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN
+    VLLM_ATTENTION_BACKEND=TRITON_ATTN python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
    cd tests
    pytest -v -s v1/core
    pytest -v -s v1/engine
@@ -47,6 +46,6 @@ docker run \
    pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
    pytest -v -s v1/structured_output
    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py
-    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py
+    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_shared_storage_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py
    pytest -v -s v1/test_serial_utils.py
 '
--- a/.buildkite/scripts/run-multi-node-test.sh
+++ b/.buildkite/scripts/run-multi-node-test.sh
@@ -2,17 +2,6 @@

 set -euox pipefail

-# To detect ROCm
-# Check multiple indicators:
-if [ -e /dev/kfd ] || \
-    [ -d /opt/rocm ] || \
-    command -v rocm-smi &> /dev/null || \
-    [ -n "${ROCM_HOME:-}" ]; then
-    IS_ROCM=1
-else
-    IS_ROCM=0
-fi
-
 if [[ $# -lt 4 ]]; then
    echo "Usage: .buildkite/scripts/run-multi-node-test.sh WORKING_DIR NUM_NODES NUM_GPUS DOCKER_IMAGE COMMAND1 COMMAND2 ... COMMANDN"
    exit 1
@@ -37,18 +26,13 @@ for command in "${COMMANDS[@]}"; do
    echo "$command"
 done

-
 start_network() {
    docker network create --subnet=192.168.10.0/24 docker-net
 }

 start_nodes() {
    for node in $(seq 0 $(($NUM_NODES-1))); do
-        if [ "$IS_ROCM" -eq 1 ]; then
-            GPU_DEVICES='--device /dev/kfd --device /dev/dri -e HIP_VISIBLE_DEVICES='
-        else
-            GPU_DEVICES='--gpus "device='
-        fi
+        GPU_DEVICES='"device='
        for node_gpu in $(seq 0 $(($NUM_GPUS - 1))); do
            DEVICE_NUM=$(($node * $NUM_GPUS + $node_gpu))
            GPU_DEVICES+=$(($DEVICE_NUM))
@@ -56,9 +40,7 @@ start_nodes() {
                GPU_DEVICES+=','
            fi
        done
-        if [ "$IS_ROCM" -eq 0 ]; then
-            GPU_DEVICES+='"'
-        fi
+        GPU_DEVICES+='"'

        # start the container in detached mode
        # things to note:
@@ -67,7 +49,7 @@ start_nodes() {
        # 3. map the huggingface cache directory to the container
        # 3. assign ip addresses to the containers (head node: 192.168.10.10, worker nodes:
        #    starting from 192.168.10.11)
-        docker run -d $GPU_DEVICES --shm-size=10.24gb -e HF_TOKEN \
+        docker run -d --gpus "$GPU_DEVICES" --shm-size=10.24gb -e HF_TOKEN \
            -v ~/.cache/huggingface:/root/.cache/huggingface --name "node$node" \
            --network docker-net --ip 192.168.10.$((10 + $node)) --rm "$DOCKER_IMAGE" \
            /bin/bash -c "tail -f /dev/null"
--- a/.buildkite/scripts/run-prime-rl-test.sh
+++ b/.buildkite/scripts/run-prime-rl-test.sh
@@ -12,11 +12,6 @@ REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
 PRIME_RL_REPO="https://github.com/PrimeIntellect-ai/prime-rl.git"
 PRIME_RL_DIR="${REPO_ROOT}/prime-rl"

-if command -v rocm-smi &> /dev/null || command -v rocminfo &> /dev/null; then
-    echo "AMD GPU detected. Prime-RL currently only supports NVIDIA. Skipping..."
-    exit 0
-fi
-
 echo "Setting up Prime-RL integration test environment..."

 # Clean up any existing Prime-RL directory
--- a/.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh
+++ b/.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh
@@ -17,17 +17,7 @@ wait_for_server() {
 }

 MODEL="deepseek-ai/DeepSeek-V2-lite"
-
-# Set BACKENDS based on platform
-if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:-}" ]]; then
-  # ROCm platform
-  BACKENDS=("allgather_reducescatter")
-  # Disable MOE padding for ROCm since it is causing eplb to fail
-  export VLLM_ROCM_MOE_PADDING=0
-else
-  # Non-ROCm platform (CUDA/other)
-  BACKENDS=("deepep_high_throughput" "deepep_low_latency")
-fi
+BACKENDS=("deepep_high_throughput" "deepep_low_latency")

 cleanup() {
  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
--- a/.buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh
+++ b/.buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh
@@ -2,9 +2,9 @@
 set -euxo pipefail

 # args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
-THRESHOLD=${1:-0.25}
+THRESHOLD=${1:-0.8}
 NUM_Q=${2:-1319}
-PORT=${3:-8040}
+PORT=${3:-8020}
 OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
 mkdir -p "${OUT_DIR}"

@@ -16,18 +16,8 @@ wait_for_server() {
    done'
 }

-MODEL="Qwen/Qwen3-Next-80B-A3B-Instruct"
-
-# Set BACKENDS based on platform
-if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:-}" ]]; then
-  # ROCm platform
-  BACKENDS=("allgather_reducescatter")
-  # Disable MOE padding for ROCm since it is causing eplb to fail
-  export VLLM_ROCM_MOE_PADDING=0
-else
-  # Non-ROCm platform (CUDA/other)
-  BACKENDS=("deepep_high_throughput" "deepep_low_latency")
-fi
+MODEL="QWen/Qwen3-30B-A3B-FP8"
+BACKENDS=("deepep_high_throughput" "deepep_low_latency")

 cleanup() {
  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
@@ -43,17 +33,14 @@ trap cleanup EXIT

 for BACK in "${BACKENDS[@]}"; do
  VLLM_DEEP_GEMM_WARMUP=skip \
+  VLLM_ALL2ALL_BACKEND=$BACK \
  vllm serve "$MODEL" \
    --enforce-eager \
-    --tensor-parallel-size 4 \
+    --tensor-parallel-size 2 \
+    --data-parallel-size 2 \
    --enable-expert-parallel \
-    --enable-eplb \
-    --all2all-backend $BACK \
-    --eplb-config '{"window_size":200,"step_interval":600,"use_async":true}' \
-    --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":1}' \
    --trust-remote-code \
    --max-model-len 2048 \
-    --gpu-memory-utilization 0.9 \
    --port $PORT &
  SERVER_PID=$!
  wait_for_server $PORT
--- a/.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh
+++ b/.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh
@@ -1,74 +0,0 @@
-#!/usr/bin/env bash
-set -euxo pipefail
-
-# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT] [DATA_PARALLEL_SIZE] [TENSOR_PARALLEL_SIZE]
-THRESHOLD=${1:-0.8}
-NUM_Q=${2:-1319}
-PORT=${3:-8020}
-DATA_PARALLEL_SIZE=${4:-2}
-TENSOR_PARALLEL_SIZE=${5:-2}
-OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
-mkdir -p "${OUT_DIR}"
-
-wait_for_server() {
-  local port=$1
-  timeout 600 bash -c '
-    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
-      sleep 1
-    done'
-}
-
-MODEL="QWen/Qwen3-30B-A3B-FP8"
-# Set BACKENDS based on platform
-if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:-}" ]]; then
-  # ROCm platform
-  BACKENDS=("allgather_reducescatter")
-  # Disable MOE padding for ROCm since it is causing eplb to fail
-  export VLLM_ROCM_MOE_PADDING=0
-else
-  # Non-ROCm platform (CUDA/other)
-  BACKENDS=("deepep_high_throughput" "deepep_low_latency")
-fi
-
-cleanup() {
-  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
-    kill "${SERVER_PID}" 2>/dev/null || true
-    for _ in {1..20}; do
-      kill -0 "${SERVER_PID}" 2>/dev/null || break
-      sleep 0.5
-    done
-    kill -9 "${SERVER_PID}" 2>/dev/null || true
-  fi
-}
-trap cleanup EXIT
-
-for BACK in "${BACKENDS[@]}"; do
-  VLLM_DEEP_GEMM_WARMUP=skip \
-  vllm serve "$MODEL" \
-    --enforce-eager \
-    --enable-eplb \
-    --all2all-backend $BACK \
-    --eplb-config '{"window_size":10, "step_interval":100, "num_redundant_experts":0, "log_balancedness":true}' \
-    --tensor-parallel-size ${TENSOR_PARALLEL_SIZE} \
-    --data-parallel-size ${DATA_PARALLEL_SIZE} \
-    --enable-expert-parallel \
-    --trust-remote-code \
-    --max-model-len 2048 \
-    --port $PORT &
-  SERVER_PID=$!
-  wait_for_server $PORT
-
-  TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
-  OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
-  python3 - <<PY
-import json; acc=json.load(open('${OUT}'))['accuracy']
-print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
-assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
-PY
-
-  cleanup
-  SERVER_PID=
-  sleep 1
-  PORT=$((PORT+1))
-done
--- a/.buildkite/scripts/upload-nightly-wheels.sh
+++ b/.buildkite/scripts/upload-nightly-wheels.sh
@@ -1,108 +0,0 @@
-#!/usr/bin/env bash
-
-set -ex
-
-# ======== part 0: setup ========
-
-BUCKET="vllm-wheels"
-INDICES_OUTPUT_DIR="indices"
-DEFAULT_VARIANT_ALIAS="cu129" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
-PYTHON=${PYTHON_PROG:=python3} # try to read from env var, otherwise use python3
-SUBPATH=$BUILDKITE_COMMIT
-S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
-
-# detect if python3.10+ is available
-has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)")
-if [[ "$has_new_python" -eq 0 ]]; then
-    # use new python from docker
-    docker pull python:3-slim
-    PYTHON="docker run --rm -v $(pwd):/app -w /app python:3-slim python3"
-fi
-
-echo "Using python interpreter: $PYTHON"
-echo "Python version: $($PYTHON --version)"
-
-# ========= part 1: collect, rename & upload the wheel ==========
-
-# Assume wheels are in artifacts/dist/*.whl
-wheel_files=(artifacts/dist/*.whl)
-
-# Check that exactly one wheel is found
-if [[ ${#wheel_files[@]} -ne 1 ]]; then
-  echo "Error: Expected exactly one wheel file in artifacts/dist/, but found ${#wheel_files[@]}"
-  exit 1
-fi
-wheel="${wheel_files[0]}"
-
-# default build image uses ubuntu 20.04, which corresponds to manylinux_2_31
-# we also accept params as manylinux tag
-# refer to https://github.com/mayeut/pep600_compliance?tab=readme-ov-file#acceptable-distros-to-build-wheels
-manylinux_version="${1:-manylinux_2_31}"
-
-# Rename 'linux' to the appropriate manylinux version in the wheel filename
-if [[ "$wheel" != *"linux"* ]]; then
-  echo "Error: Wheel filename does not contain 'linux': $wheel"
-  exit 1
-fi
-new_wheel="${wheel/linux/$manylinux_version}"
-mv -- "$wheel" "$new_wheel"
-wheel="$new_wheel"
-echo "Renamed wheel to: $wheel"
-
-# Extract the version from the wheel
-version=$(unzip -p "$wheel" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
-echo "Version in wheel: $version"
-pure_version="${version%%+*}"
-echo "Pure version (without variant): $pure_version"
-
-# copy wheel to its own bucket
-aws s3 cp "$wheel" "$S3_COMMIT_PREFIX"
-
-# ========= part 2: generate and upload indices ==========
-# generate indices for all existing wheels in the commit directory
-# this script might be run multiple times if there are multiple variants being built
-# so we need to guarantee there is little chance for "TOCTOU" issues
-# i.e., one process is generating indices while another is uploading a new wheel
-# so we need to ensure no time-consuming operations happen below
-
-# list all wheels in the commit directory
-echo "Existing wheels on S3:"
-aws s3 ls "$S3_COMMIT_PREFIX"
-obj_json="objects.json"
-aws s3api list-objects-v2 --bucket "$BUCKET" --prefix "$SUBPATH/" --delimiter / --output json > "$obj_json"
-mkdir -p "$INDICES_OUTPUT_DIR"
-
-# call script to generate indicies for all existing wheels
-# this indices have relative paths that could work as long as it is next to the wheel directory in s3
-# i.e., the wheels are always in s3://vllm-wheels/<commit>/
-# and indices can be placed in /<commit>/, or /nightly/, or /<version>/
-if [[ ! -z "$DEFAULT_VARIANT_ALIAS" ]]; then
-    alias_arg="--alias-to-default $DEFAULT_VARIANT_ALIAS"
-else
-    alias_arg=""
-fi
-
-# HACK: we do not need regex module here, but it is required by pre-commit hook
-# To avoid any external dependency, we simply replace it back to the stdlib re module
-sed -i 's/import regex as re/import re/g' .buildkite/scripts/generate-nightly-index.py
-$PYTHON .buildkite/scripts/generate-nightly-index.py --version "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "commit $BUILDKITE_COMMIT" $alias_arg
-
-# copy indices to /<commit>/ unconditionally
-echo "Uploading indices to $S3_COMMIT_PREFIX"
-aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "$S3_COMMIT_PREFIX"
-
-# copy to /nightly/ only if it is on the main branch and not a PR 
-if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]]; then
-    echo "Uploading indices to overwrite /nightly/"
-    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/nightly/"
-fi
-
-# re-generate and copy to /<pure_version>/ only if it does not have "dev" in the version
-if [[ "$version" != *"dev"* ]]; then
-    echo "Re-generating indices for /$pure_version/"
-    rm -rf "$INDICES_OUTPUT_DIR/*"
-    mkdir -p "$INDICES_OUTPUT_DIR"
-    # wheel-dir is overridden to be the commit directory, so that the indices point to the correct wheel path
-    $PYTHON .buildkite/scripts/generate-nightly-index.py --version "$pure_version" --wheel-dir "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "version $pure_version" $alias_arg
-    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/$pure_version/"
-fi
--- a/.buildkite/scripts/upload-release-wheels.sh
+++ b/.buildkite/scripts/upload-release-wheels.sh
@@ -1,103 +0,0 @@
-#!/usr/bin/env bash
-
-set -e
-
-BUCKET="vllm-wheels"
-SUBPATH=$BUILDKITE_COMMIT
-S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
-
-RELEASE_VERSION=$(buildkite-agent meta-data get release-version)
-echo "Release version from Buildkite: $RELEASE_VERSION"
-GIT_VERSION=$(git describe --exact-match --tags $BUILDKITE_COMMIT 2>/dev/null)
-if [ -z "$GIT_VERSION" ]; then
-    echo "[FATAL] Not on a git tag, cannot create release."
-    exit 1
-else
-    echo "Git version for commit $BUILDKITE_COMMIT: $GIT_VERSION"
-fi
-# sanity check for version mismatch
-if [ "v$RELEASE_VERSION" != "$GIT_VERSION" ]; then
-  if [ "$FORCE_RELEASE_IGNORE_VERSION_MISMATCH" == "true" ]; then
-    echo "[WARNING] Force release and ignore version mismatch"
-  else
-    echo "[FATAL] Release version from Buildkite does not match Git version."
-    exit 1
-  fi
-fi
-
-# check pypi token
-if [ -z "$PYPI_TOKEN" ]; then
-  echo "[FATAL] PYPI_TOKEN is not set."
-  exit 1
-else
-  export TWINE_USERNAME="__token__"
-  export TWINE_PASSWORD="$PYPI_TOKEN"
-fi
-
-# check github token
-if [ -z "$GITHUB_TOKEN" ]; then
-  echo "[FATAL] GITHUB_TOKEN is not set."
-  exit 1
-else
-  export GH_TOKEN="$GITHUB_TOKEN"
-fi
-
-set -x # avoid printing secrets above
-
-# download gh CLI from github
-# Get latest gh CLI version from GitHub API
-GH_VERSION=$(curl -s https://api.github.com/repos/cli/cli/releases/latest | grep '"tag_name":' | sed -E 's/.*"([^"]+)".*/\1/' | sed 's/^v//')
-if [ -z "$GH_VERSION" ]; then
-  echo "[FATAL] Failed to get latest gh CLI version from GitHub"
-  exit 1
-fi
-echo "Downloading gh CLI version: $GH_VERSION"
-GH_TARBALL="gh_${GH_VERSION}_linux_amd64.tar.gz"
-GH_URL="https://github.com/cli/cli/releases/download/v${GH_VERSION}/${GH_TARBALL}"
-GH_INSTALL_DIR="/tmp/gh-install"
-mkdir -p "$GH_INSTALL_DIR"
-pushd "$GH_INSTALL_DIR"
-curl -L -o "$GH_TARBALL" "$GH_URL"
-tar -xzf "$GH_TARBALL"
-GH_BIN=$(realpath $(find . -name "gh" -type f -executable | head -n 1))
-if [ -z "$GH_BIN" ]; then
-  echo "[FATAL] Failed to find gh CLI executable"
-  exit 1
-fi
-echo "gh CLI downloaded successfully, version: $($GH_BIN --version)"
-echo "Last 5 releases on GitHub:" # as a sanity check of gh and GH_TOKEN
-command "$GH_BIN" release list --limit 5
-popd
-
-# install twine from pypi
-python3 -m venv /tmp/vllm-release-env
-source /tmp/vllm-release-env/bin/activate
-pip install twine
-python3 -m twine --version
-
-# copy release wheels to local directory
-DIST_DIR=/tmp/vllm-release-dist
-echo "Existing wheels on S3:"
-aws s3 ls "$S3_COMMIT_PREFIX"
-echo "Copying wheels to local directory"
-mkdir -p $DIST_DIR
-# include only wheels for the release version, ignore all files with "dev" or "rc" in the name
-aws s3 cp --recursive --exclude "*" --include "vllm-${RELEASE_VERSION}*.whl" --exclude "*dev*" --exclude "*rc*" "$S3_COMMIT_PREFIX" $DIST_DIR
-echo "Wheels copied to local directory"
-# generate source tarball
-git archive --format=tar.gz --output="$DIST_DIR/vllm-${RELEASE_VERSION}.tar.gz" $BUILDKITE_COMMIT
-ls -la $DIST_DIR
-
-
-# upload wheels to PyPI (only default variant, i.e. files without '+' in the name)
-PYPI_WHEEL_FILES=$(find $DIST_DIR -name "vllm-${RELEASE_VERSION}*.whl" -not -name "*+*")
-if [ -z "$PYPI_WHEEL_FILES" ]; then
-  echo "No default variant wheels found, quitting..."
-  exit 1
-fi
-python3 -m twine check $PYPI_WHEEL_FILES
-python3 -m twine --non-interactive --verbose upload $PYPI_WHEEL_FILES
-echo "Wheels uploaded to PyPI"
-
-# create release on GitHub with the release version and all wheels
-command "$GH_BIN" release create $GIT_VERSION -d --latest --notes-from-tag --verify-tag $DIST_DIR/*.whl
--- a/.buildkite/scripts/upload-rocm-wheels.sh
+++ b/.buildkite/scripts/upload-rocm-wheels.sh
@@ -1,151 +0,0 @@
-#!/usr/bin/env bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Upload ROCm wheels to S3 with proper index generation
-#
-# Required environment variables:
-#   AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY (or IAM role)
-#   S3_BUCKET (default: vllm-wheels)
-#
-# S3 path structure:
-#   s3://vllm-wheels/rocm/{commit}/     - All wheels for this commit
-#   s3://vllm-wheels/rocm/nightly/      - Index pointing to latest nightly
-#   s3://vllm-wheels/rocm/{version}/    - Index for release versions
-
-set -ex
-
-# ======== Configuration ========
-BUCKET="${S3_BUCKET:-vllm-wheels}"
-ROCM_SUBPATH="rocm/${BUILDKITE_COMMIT}"
-S3_COMMIT_PREFIX="s3://$BUCKET/$ROCM_SUBPATH/"
-INDICES_OUTPUT_DIR="rocm-indices"
-PYTHON="${PYTHON_PROG:-python3}"
-
-# ROCm uses manylinux_2_35 (Ubuntu 22.04 based)
-MANYLINUX_VERSION="manylinux_2_35"
-
-echo "========================================"
-echo "ROCm Wheel Upload Configuration"
-echo "========================================"
-echo "S3 Bucket: $BUCKET"
-echo "S3 Path: $ROCM_SUBPATH"
-echo "Commit: $BUILDKITE_COMMIT"
-echo "Branch: $BUILDKITE_BRANCH"
-echo "========================================"
-
-# ======== Part 0: Setup Python ========
-
-# Detect if python3.12+ is available
-has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)" 2>/dev/null || echo 0)
-if [[ "$has_new_python" -eq 0 ]]; then
-    # Use new python from docker
-    # Use --user to ensure files are created with correct ownership (not root)
-    docker pull python:3-slim
-    PYTHON="docker run --rm --user $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
-fi
-
-echo "Using python interpreter: $PYTHON"
-echo "Python version: $($PYTHON --version)"
-
-# ======== Part 1: Collect and prepare wheels ========
-
-# Collect all wheels
-mkdir -p all-rocm-wheels
-cp artifacts/rocm-base-wheels/*.whl all-rocm-wheels/ 2>/dev/null || true
-cp artifacts/rocm-vllm-wheel/*.whl all-rocm-wheels/ 2>/dev/null || true
-
-WHEEL_COUNT=$(ls all-rocm-wheels/*.whl 2>/dev/null | wc -l)
-echo "Total wheels to upload: $WHEEL_COUNT"
-
-if [ "$WHEEL_COUNT" -eq 0 ]; then
-    echo "ERROR: No wheels found to upload!"
-    exit 1
-fi
-
-# Rename linux to manylinux in wheel filenames
-for wheel in all-rocm-wheels/*.whl; do
-    if [[ "$wheel" == *"linux"* ]] && [[ "$wheel" != *"manylinux"* ]]; then
-        new_wheel="${wheel/linux/$MANYLINUX_VERSION}"
-        mv -- "$wheel" "$new_wheel"
-        echo "Renamed: $(basename "$wheel") -> $(basename "$new_wheel")"
-    fi
-done
-
-echo ""
-echo "Wheels to upload:"
-ls -lh all-rocm-wheels/
-
-# ======== Part 2: Upload wheels to S3 ========
-
-echo ""
-echo "Uploading wheels to $S3_COMMIT_PREFIX"
-for wheel in all-rocm-wheels/*.whl; do
-    aws s3 cp "$wheel" "$S3_COMMIT_PREFIX"
-done
-
-# ======== Part 3: Generate and upload indices ========
-
-# List existing wheels in commit directory
-echo ""
-echo "Generating indices..."
-obj_json="rocm-objects.json"
-aws s3api list-objects-v2 --bucket "$BUCKET" --prefix "$ROCM_SUBPATH/" --delimiter / --output json > "$obj_json"
-
-mkdir -p "$INDICES_OUTPUT_DIR"
-
-# Use the existing generate-nightly-index.py
-# HACK: Replace regex module with stdlib re (same as CUDA script)
-sed -i 's/import regex as re/import re/g' .buildkite/scripts/generate-nightly-index.py
-
-$PYTHON .buildkite/scripts/generate-nightly-index.py \
-    --version "$ROCM_SUBPATH" \
-    --current-objects "$obj_json" \
-    --output-dir "$INDICES_OUTPUT_DIR" \
-    --comment "ROCm commit $BUILDKITE_COMMIT"
-
-# Upload indices to commit directory
-echo "Uploading indices to $S3_COMMIT_PREFIX"
-aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "$S3_COMMIT_PREFIX"
-
-# Update rocm/nightly/ if on main branch and not a PR
-if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]] || [[ "$NIGHTLY" == "1" ]]; then
-    echo "Updating rocm/nightly/ index..."
-    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/rocm/nightly/"
-fi
-
-# Extract version from vLLM wheel and update version-specific index
-VLLM_WHEEL=$(ls all-rocm-wheels/vllm*.whl 2>/dev/null | head -1)
-if [ -n "$VLLM_WHEEL" ]; then
-    VERSION=$(unzip -p "$VLLM_WHEEL" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
-    echo "Version in wheel: $VERSION"
-    PURE_VERSION="${VERSION%%+*}"
-    PURE_VERSION="${PURE_VERSION%%.rocm}"
-    echo "Pure version: $PURE_VERSION"
-
-    if [[ "$VERSION" != *"dev"* ]]; then
-        echo "Updating rocm/$PURE_VERSION/ index..."
-        aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/rocm/$PURE_VERSION/"
-    fi
-fi
-
-# ======== Part 4: Summary ========
-
-echo ""
-echo "========================================"
-echo "ROCm Wheel Upload Complete!"
-echo "========================================"
-echo ""
-echo "Wheels available at:"
-echo "  s3://$BUCKET/$ROCM_SUBPATH/"
-echo ""
-echo "Install command (by commit):"
-echo "  pip install vllm --extra-index-url https://${BUCKET}.s3.amazonaws.com/$ROCM_SUBPATH/"
-echo ""
-if [[ "$BUILDKITE_BRANCH" == "main" ]] || [[ "$NIGHTLY" == "1" ]]; then
-    echo "Install command (nightly):"
-    echo "  pip install vllm --extra-index-url https://${BUCKET}.s3.amazonaws.com/rocm/nightly/"
-fi
-echo ""
-echo "Wheel count: $WHEEL_COUNT"
-echo "========================================"
--- a/.buildkite/scripts/upload-wheels.sh
+++ b/.buildkite/scripts/upload-wheels.sh
@@ -0,0 +1,83 @@
+#!/usr/bin/env bash
+
+set -ex
+
+# Assume wheels are in artifacts/dist/*.whl
+wheel_files=(artifacts/dist/*.whl)
+
+# Check that exactly one wheel is found
+if [[ ${#wheel_files[@]} -ne 1 ]]; then
+  echo "Error: Expected exactly one wheel file in artifacts/dist/, but found ${#wheel_files[@]}"
+  exit 1
+fi
+
+# Get the single wheel file
+wheel="${wheel_files[0]}"
+
+# Detect architecture and rename 'linux' to appropriate manylinux version
+arch=$(uname -m)
+if [[ $arch == "x86_64" ]]; then
+    manylinux_version="manylinux1"
+elif [[ $arch == "aarch64" ]]; then
+    manylinux_version="manylinux2014"
+else
+    echo "Warning: Unknown architecture $arch, using manylinux1 as default"
+    manylinux_version="manylinux1"
+fi
+
+# Rename 'linux' to the appropriate manylinux version in the wheel filename
+new_wheel="${wheel/linux/$manylinux_version}"
+mv -- "$wheel" "$new_wheel"
+wheel="$new_wheel"
+
+# Extract the version from the wheel
+version=$(unzip -p "$wheel" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
+echo "Version: $version"
+
+normal_wheel="$wheel" # Save the original wheel filename
+
+# If the version contains "dev", rename it to v1.0.0.dev for consistency
+if [[ $version == *dev* ]]; then
+    suffix="${version##*.}"
+    if [[ $suffix == cu* ]]; then
+        new_version="1.0.0.dev+${suffix}"
+    else
+        new_version="1.0.0.dev"
+    fi
+    new_wheel="${wheel/$version/$new_version}"
+    # use cp to keep both files in the artifacts directory
+    cp -- "$wheel" "$new_wheel"
+    wheel="$new_wheel"
+    version="$new_version"
+fi
+
+# Upload the wheel to S3
+python3 .buildkite/generate_index.py --wheel "$normal_wheel"
+
+# generate index for this commit
+aws s3 cp "$wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
+aws s3 cp "$normal_wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
+
+if [[ $normal_wheel == *"cu129"* ]]; then
+    # only upload index.html for cu129 wheels (default wheels) as it
+    # is available on both x86 and arm64
+    aws s3 cp index.html "s3://vllm-wheels/$BUILDKITE_COMMIT/vllm/index.html"
+    aws s3 cp "s3://vllm-wheels/nightly/index.html" "s3://vllm-wheels/$BUILDKITE_COMMIT/index.html"
+else
+    echo "Skipping index files for non-cu129 wheels"
+fi
+
+# generate index for nightly
+aws s3 cp "$wheel" "s3://vllm-wheels/nightly/"
+aws s3 cp "$normal_wheel" "s3://vllm-wheels/nightly/"
+
+if [[ $normal_wheel == *"cu129"* ]]; then
+    # only upload index.html for cu129 wheels (default wheels) as it
+    # is available on both x86 and arm64
+    aws s3 cp index.html "s3://vllm-wheels/nightly/vllm/index.html"
+else
+    echo "Skipping index files for non-cu129 wheels"
+fi
+
+aws s3 cp "$wheel" "s3://vllm-wheels/$version/"
+aws s3 cp index.html "s3://vllm-wheels/$version/vllm/index.html"
--- a/.buildkite/test-amd.yaml
+++ b/.buildkite/test-amd.yaml
--- a/.buildkite/test-pipeline.yaml
+++ b/.buildkite/test-pipeline.yaml
@@ -57,16 +57,14 @@ steps:
  - pytest -v -s -m 'not cpu_test' multimodal
  - pytest -v -s utils_

- label: Async Engine, Inputs, Utils, Worker, Config Test (CPU) # 20min
-  timeout_in_minutes: 30
+- label: Async Engine, Inputs, Utils, Worker, Config Test (CPU) # 4 mins
+  timeout_in_minutes: 10
  source_file_dependencies:
  - vllm/
  - tests/test_inputs.py
  - tests/test_outputs.py
  - tests/multimodal
  - tests/standalone_tests/lazy_imports.py
-  - tests/tokenizers_
-  - tests/tool_parsers
  - tests/transformers_utils
  - tests/config
  no_gpu: true
@@ -75,8 +73,6 @@ steps:
  - pytest -v -s test_inputs.py
  - pytest -v -s test_outputs.py
  - pytest -v -s -m 'cpu_test' multimodal
-  - pytest -v -s tokenizers_
-  - pytest -v -s tool_parsers
  - pytest -v -s transformers_utils
  - pytest -v -s config

@@ -114,7 +110,7 @@ steps:
  - tests/entrypoints/
  commands:
  - pytest -v -s entrypoints/openai/tool_parsers
-  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
+  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling

 - label: Entrypoints Integration Test (LLM) # 30min
  timeout_in_minutes: 40
@@ -132,7 +128,7 @@ steps:
  - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
  - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests

- label: Entrypoints Integration Test (API Server 1) # 100min
+- label: Entrypoints Integration Test (API Server) # 100min
  timeout_in_minutes: 130
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
@@ -144,26 +140,10 @@ steps:
  - tests/entrypoints/test_chat_utils
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/  --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses
+  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/openai/test_collective_rpc.py # PYTHONPATH is needed to import custom Worker extension
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/test_collective_rpc.py --ignore=entrypoints/openai/tool_parsers/
  - pytest -v -s entrypoints/test_chat_utils.py

- label: Entrypoints Integration Test (API Server 2)
-  timeout_in_minutes: 50
-  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/tests"
-  fast_check: true
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/sleep
-  - tests/entrypoints/rpc
-  - tests/tool_use
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/sleep
-  - PYTHONPATH=/vllm-workspace  pytest -v -s entrypoints/rpc
-  - pytest -v -s tool_use
-
 - label: Entrypoints Integration Test (Pooling)
  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental]
@@ -177,18 +157,6 @@ steps:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - pytest -v -s entrypoints/pooling

- label: Entrypoints Integration Test (Responses API)
-  timeout_in_minutes: 50
-  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/tests"
-  fast_check: true
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/openai/responses
-  commands:
-  - pytest -v -s entrypoints/openai/responses
-
 - label: Distributed Tests (4 GPUs) # 35min
  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental]
@@ -199,7 +167,7 @@ steps:
  - tests/distributed/test_utils
  - tests/distributed/test_pynccl
  - tests/distributed/test_events
-  - tests/compile/fullgraph/test_basic_correctness.py
+  - tests/compile/test_basic_correctness
  - examples/offline_inference/rlhf.py
  - examples/offline_inference/rlhf_colocate.py
  - tests/examples/offline_inference/data_parallel.py
@@ -224,13 +192,12 @@ steps:
  # test with internal dp
  - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py
  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py
  - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
  - pytest -v -s distributed/test_utils.py
-  - pytest -v -s compile/fullgraph/test_basic_correctness.py
+  - pytest -v -s compile/test_basic_correctness.py
  - pytest -v -s distributed/test_pynccl.py
  - pytest -v -s distributed/test_events.py
  - pytest -v -s distributed/test_symm_mem_allreduce.py
@@ -308,18 +275,21 @@ steps:
  - pytest -v -s test_regression.py
  working_dir: "/vllm-workspace/tests" # optional

- label: Engine Test # 9min
-  timeout_in_minutes: 15
+- label: Engine Test # 25min
+  timeout_in_minutes: 40
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
  - tests/engine
+  - tests/tokenization
  - tests/test_sequence
  - tests/test_config
  - tests/test_logger
  - tests/test_vllm_port
  commands:
  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
+  # OOM in the CI unless we run this separately
+  - pytest -v -s tokenization

 - label: V1 Test e2e + engine # 30min
  timeout_in_minutes: 45
@@ -331,10 +301,7 @@ steps:
    # TODO: accuracy does not match, whether setting
    # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
    - pytest -v -s v1/e2e
-    # Run this test standalone for now;
-    # need to untangle use (implicit) use of spawn/fork across the tests.
-    - pytest -v -s v1/engine/test_preprocess_error_handling.py
-    - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
+    - pytest -v -s v1/engine

 - label: V1 Test entrypoints # 35min
  timeout_in_minutes: 50
@@ -379,19 +346,6 @@ steps:
  commands:
    - pytest -v -s v1/attention

- label: Batch Invariance Tests (H100) # 10min
-  timeout_in_minutes: 25
-  gpu: h100
-  source_file_dependencies:
-    - vllm/v1/attention
-    - vllm/model_executor/layers
-    - tests/v1/determinism/
-  commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pip install pytest-timeout pytest-forked
-    - pytest -v -s v1/determinism/test_batch_invariance.py
-    - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
-
 - label: V1 Test attention (B200) # 10min
  timeout_in_minutes: 30
  gpu: b200
@@ -421,28 +375,23 @@ steps:
  working_dir: "/vllm-workspace/examples"
  source_file_dependencies:
  - vllm/entrypoints
-  - vllm/multimodal
  - examples/
  commands:
    - pip install tensorizer # for tensorizer test
-    # for basic
-    - python3 offline_inference/basic/chat.py
    - python3 offline_inference/basic/generate.py --model facebook/opt-125m
    - python3 offline_inference/basic/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
+    - python3 offline_inference/basic/chat.py
+    - python3 offline_inference/prefix_caching.py
+    - python3 offline_inference/llm_engine_example.py
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_pooling.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
    - python3 offline_inference/basic/classify.py
    - python3 offline_inference/basic/embed.py
    - python3 offline_inference/basic/score.py
-    # for multi-modal models
-    - python3 offline_inference/audio_language.py --seed 0
-    - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
-    # for pooling models
-    - python3 pooling/pooling/vision_language_pooling.py --seed 0
-    # for features demo
-    - python3 offline_inference/prefix_caching.py
-    - python3 offline_inference/llm_engine_example.py
-    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
    # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
@@ -496,14 +445,18 @@ steps:
    - vllm/
    - tests/compile
  commands:
-  # Run unit tests defined directly under compile/,
-  # not including subdirectories, which are usually heavier
-  # tests covered elsewhere.
-  # Use `find` to launch multiple instances of pytest so that
-  # they do not suffer from https://github.com/vllm-project/vllm/issues/28965
-  # However, find does not normally propagate error codes, so we combine it with xargs
-  # (using -0 for proper path handling)
-  - "find compile/ -maxdepth 1 -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
+    - pytest -v -s compile/test_graph_partition.py
+    - pytest -v -s compile/test_config.py
+    - pytest -v -s compile/test_pass_manager.py
+    - pytest -v -s compile/test_fusion.py
+    - pytest -v -s compile/test_fusion_attn.py
+    - pytest -v -s compile/test_functionalization.py
+    - pytest -v -s compile/test_silu_mul_quant_fusion.py
+    - pytest -v -s compile/test_fusion_all_reduce.py
+    - pytest -v -s compile/test_decorator.py
+    - pytest -v -s compile/test_noop_elimination.py
+    - pytest -v -s compile/test_aot_compile.py
+    - pytest -v -s compile/test_qk_norm_rope_fusion.py

 - label: PyTorch Fullgraph Smoke Test # 15min
  timeout_in_minutes: 30
@@ -513,13 +466,9 @@ steps:
  - vllm/
  - tests/compile
  commands:
-  # Run smoke tests under fullgraph directory, except test_full_graph.py
-  # as it is a heavy test that is covered in other steps.
-  # Use `find` to launch multiple instances of pytest so that
-  # they do not suffer from https://github.com/vllm-project/vllm/issues/28965
-  # However, find does not normally propagate error codes, so we combine it with xargs
-  # (using -0 for proper path handling)
-  - "find compile/fullgraph -maxdepth 1 -name 'test_*.py' -not -name 'test_full_graph.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
+  - pytest -v -s compile/test_basic_correctness.py
+  - pytest -v -s compile/test_multimodal_compile.py
+  - pytest -v -s compile/piecewise/

 - label: PyTorch Fullgraph Test # 27min
  timeout_in_minutes: 40
@@ -530,10 +479,10 @@ steps:
  - tests/compile
  commands:
    # fp8 kv scales not supported on sm89, tested on Blackwell instead
-  - pytest -v -s compile/fullgraph/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
+  - pytest -v -s compile/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
    # Limit to no custom ops to reduce running time
    # Wrap with quotes to escape yaml and avoid starting -k string with a -
-  - "pytest -v -s compile/distributed/test_fusions_e2e.py -k 'TRITON and not +quant_fp8 and not Llama-4'"
+  - "pytest -v -s compile/test_fusions_e2e.py -k 'TRITON and not +quant_fp8 and not Llama-4'"

 - label: Cudagraph test
  timeout_in_minutes: 20
@@ -605,25 +554,6 @@ steps:
  commands:
    - pytest -v -s kernels/mamba

- label: Kernels DeepGEMM Test (H100)
-  timeout_in_minutes: 45
-  gpu: h100
-  num_gpus: 1
-  source_file_dependencies:
-  - tools/install_deepgemm.sh
-  - vllm/utils/deep_gemm.py
-  - vllm/model_executor/layers/fused_moe
-  - vllm/model_executor/layers/quantization
-  - tests/kernels/quantization/test_block_fp8.py
-  - tests/kernels/moe/test_deepgemm.py
-  - tests/kernels/moe/test_batched_deepgemm.py
-  - tests/kernels/attention/test_deepgemm_attention.py
-  commands:
-    - pytest -v -s kernels/quantization/test_block_fp8.py -k deep_gemm
-    - pytest -v -s kernels/moe/test_deepgemm.py
-    - pytest -v -s kernels/moe/test_batched_deepgemm.py
-    - pytest -v -s kernels/attention/test_deepgemm_attention.py
-
 - label: Model Executor Test # 23min
  timeout_in_minutes: 35
  torch_nightly: true
@@ -673,8 +603,7 @@ steps:
  # https://github.com/pytorch/ao/issues/2919, we'll have to skip new torchao tests for now
  # we can only upgrade after this is resolved
  # TODO(jerryzh168): resolve the above comment
-  - uv pip install --system torchao==0.14.1 --index-url https://download.pytorch.org/whl/cu129
-  - uv pip install --system conch-triton-kernels
+  - uv pip install --system torchao==0.13.0 --index-url https://download.pytorch.org/whl/cu129
  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py

 - label: LM Eval Small Models # 53min
@@ -685,7 +614,7 @@ steps:
  - vllm/model_executor/layers/quantization
  autorun_on_main: true
  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt --tp-size=1

 - label: OpenAI API correctness # 22min
  timeout_in_minutes: 30
@@ -697,6 +626,25 @@ steps:
  commands: # LMEval+Transcription WER check
  - pytest -s entrypoints/openai/correctness/

+- label: OpenAI-Compatible Tool Use # 23 min
+  timeout_in_minutes: 35
+  mirror_hardwares: [amdexperimental]
+  fast_check: false
+  source_file_dependencies:
+    - vllm/
+    - tests/tool_use
+  commands:
+    - pytest -v -s -m 'not cpu_test' tool_use
+
+- label: OpenAI-Compatible Tool Use (CPU) # 5 mins
+  timeout_in_minutes: 10
+  source_file_dependencies:
+    - vllm/
+    - tests/tool_use
+  no_gpu: true
+  commands:
+    - pytest -v -s -m 'cpu_test' tool_use
+
 #####  models test  #####

 - label: Basic Models Tests (Initialization)
@@ -706,7 +654,6 @@ steps:
  source_file_dependencies:
  - vllm/
  - tests/models/test_initialization.py
-  - tests/models/registry.py
  commands:
    # Run a subset of model initialization tests
    - pytest -v -s models/test_initialization.py::test_can_initialize_small_subset
@@ -717,9 +664,7 @@ steps:
  torch_nightly: true
  source_file_dependencies:
  - vllm/model_executor/models/
-  - vllm/transformers_utils/
  - tests/models/test_initialization.py
-  - tests/models/registry.py
  commands:
    # Only when vLLM model source is modified - test initialization of a large
    # subset of supported models (the complement of the small subset in the above
@@ -845,24 +790,14 @@ steps:
  commands:
    - pytest -v -s models/language/pooling_mteb_test

- label: Multi-Modal Processor Test (CPU)
-  timeout_in_minutes: 60
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  no_gpu: true
-  commands:
-    - "pip install git+https://github.com/TIGER-AI-Lab/Mantis.git || echo 'Mantis installation skipped (decord not available on CPU-only environment)'"
-    - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py
-
- label: Multi-Modal Processor Test
+- label: Multi-Modal Processor Test # 44min
  timeout_in_minutes: 60
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/multimodal/processing/test_tensor_schema.py
+    - pytest -v -s models/multimodal/processing

 - label: Multi-Modal Models Test (Standard) # 60min
  timeout_in_minutes: 80
@@ -939,15 +874,14 @@ steps:
 - label: Transformers Nightly Models Test
  working_dir: "/vllm-workspace/"
  optional: true
-  soft_fail: true
  commands:
    - pip install --upgrade git+https://github.com/huggingface/transformers
-    - pytest -v -s tests/models/test_initialization.py
+    - pytest -v -s tests/models/test_initialization.py -k 'not (Gemma3 or ModernBert or Qwen2_5_VL or Qwen2_5vl or Qwen2VL or TransformersMultiModalEmbeddingModel or TransformersMultiModalForSequenceClassification or Ultravox or Phi4Multimodal or LlavaNextVideo or MiniCPMO or Lfm2Moe or PaliGemma or RobertaForSequenceClassification or Ovis2_5 or Fuyu or DeepseekOCR or KimiVL)'
    - pytest -v -s tests/models/test_transformers.py
-    - pytest -v -s tests/models/multimodal/processing/
-    - pytest -v -s tests/models/multimodal/test_mapping.py
+    # - pytest -v -s tests/models/multimodal/processing/
+    - pytest -v -s tests/models/multimodal/test_mapping.py -k 'not (Gemma3 or Qwen2VL or Qwen2_5_VL)'
    - python3 examples/offline_inference/basic/chat.py
-    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+    # - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
    # Whisper needs spawn method to avoid deadlock
    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper

@@ -955,6 +889,7 @@ steps:
  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/"
  gpu: b200
+  # optional: true
  source_file_dependencies:
  - csrc/quantization/fp4/
  - csrc/attention/mla/
@@ -966,8 +901,8 @@ steps:
  - vllm/v1/attention/backends/flashinfer.py
  - vllm/v1/attention/backends/mla/cutlass_mla.py
  - vllm/v1/attention/backends/mla/flashinfer_mla.py
-  - vllm/v1/attention/selector.py
  - vllm/platforms/cuda.py
+  - vllm/attention/selector.py
  commands:
    - nvidia-smi
    - python3 examples/offline_inference/basic/chat.py
@@ -990,7 +925,6 @@ steps:
    - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
    - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
    - pytest -v -s tests/kernels/moe/test_flashinfer.py
-    - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py

 - label: Blackwell Fusion and Compile Tests # 30 min
  timeout_in_minutes: 40
@@ -1000,29 +934,22 @@ steps:
  - csrc/quantization/fp4/
  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/v1/worker/
-  - vllm/v1/cudagraph_dispatcher.py
  - vllm/compilation/
  # can affect pattern matching
  - vllm/model_executor/layers/layernorm.py
  - vllm/model_executor/layers/activation.py
  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - tests/compile/test_fusion_attn.py
-  - tests/compile/test_silu_mul_quant_fusion.py
-  - tests/compile/distributed/test_fusion_all_reduce.py
-  - tests/compile/distributed/test_fusions_e2e.py
-  - tests/compile/fullgraph/test_full_graph.py
  commands:
    - nvidia-smi
    - pytest -v -s tests/compile/test_fusion_attn.py
    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
    # this runner has 2 GPUs available even though num_gpus=2 is not set
-    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
+    - pytest -v -s tests/compile/test_fusion_all_reduce.py
    # Limit to Inductor partition, no custom ops, and allreduce & attn fusion to reduce running time
    # Wrap with quotes to escape yaml
-    - "pytest -v -s tests/compile/distributed/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm -k 'True and not +quant_fp8 and not +rms_norm'"
+    - "pytest -v -s tests/compile/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm -k 'True and not +quant_fp8 and not +rms_norm'"
    # test_fp8_kv_scale_compile requires FlashAttention (not supported on default L4/L40)
-    - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
+    - pytest -v -s tests/compile/test_full_graph.py::test_fp8_kv_scale_compile

 - label: Blackwell Fusion E2E Tests # 30 min
  timeout_in_minutes: 40
@@ -1039,11 +966,12 @@ steps:
  - vllm/model_executor/layers/layernorm.py
  - vllm/model_executor/layers/activation.py
  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - tests/compile/distributed/test_fusions_e2e.py
+  - tests/compile/test_fusions_e2e.py
+  - tests/compile/test_full_graph.py
  commands:
    - nvidia-smi
    # Run all e2e fusion tests
-    - pytest -v -s tests/compile/distributed/test_fusions_e2e.py
+    - pytest -v -s tests/compile/test_fusions_e2e.py

 - label: Blackwell GPT-OSS Eval
  timeout_in_minutes: 60
@@ -1084,7 +1012,7 @@ steps:
  - csrc/
  - vllm/model_executor/layers/quantization
  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt --tp-size=1

 #####  1 GPU test  #####
 #####  multi gpus test  #####
@@ -1116,18 +1044,17 @@ steps:
  - vllm/model_executor/models/
  - tests/distributed/
  - tests/examples/offline_inference/data_parallel.py
-  - .buildkite/scripts/run-multi-node-test.sh
  commands:
  - # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
    - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed'
    - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
-    - python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code
+    - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=0 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
    - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py
    - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py
  - # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
    - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed'
    - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
-    - python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code
+    - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code

 - label: Distributed Tests (2 GPUs) # 68min
  timeout_in_minutes: 90
@@ -1142,7 +1069,7 @@ steps:
  - vllm/worker/worker_base.py
  - vllm/v1/engine/
  - vllm/v1/worker/
-  - tests/compile/fullgraph/test_basic_correctness.py
+  - tests/compile/test_basic_correctness.py
  - tests/compile/test_wrapper.py
  - tests/distributed/
  - tests/entrypoints/llm/test_collective_rpc.py
@@ -1154,11 +1081,10 @@ steps:
  # https://github.com/NVIDIA/nccl/issues/1838
  - export NCCL_CUMEM_HOST_ENABLE=0
  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
  - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
  - pytest -v -s entrypoints/llm/test_collective_rpc.py
-  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
+  - pytest -v -s ./compile/test_basic_correctness.py
  - pytest -v -s ./compile/test_wrapper.py
  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
@@ -1244,8 +1170,6 @@ steps:
    # FIXIT: find out which code initialize cuda before running the test
    # before the fix, we need to use spawn to test it
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    # Alot of these tests are on the edge of OOMing
-    - export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
    # There is some Tensor Parallelism related processing logic in LoRA that
    # requires multi-GPU testing for validation.
    - pytest -v -s -x lora/test_chatglm3_tp.py
@@ -1279,8 +1203,8 @@ steps:
  commands:
    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt

- label: NixlConnector PD accuracy tests (Distributed) # 40min
-  timeout_in_minutes: 40
+- label: NixlConnector PD accuracy tests (Distributed) # 30min
+  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
  source_file_dependencies:
@@ -1288,18 +1212,7 @@ steps:
    - tests/v1/kv_connector/nixl_integration/
  commands:
    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
- label: DP EP NixlConnector PD accuracy tests (Distributed) # 15min
-  timeout_in_minutes: 15
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+    - bash v1/kv_connector/nixl_integration/tp_config_sweep_accuracy_test.sh


 ##### multi gpus test #####
@@ -1351,23 +1264,15 @@ steps:
  working_dir: "/vllm-workspace/"
  num_gpus: 2
  commands:
-    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_async_tp.py
-    - pytest -v -s tests/compile/distributed/test_sequence_parallelism.py
-    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
-    - "VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'"
-    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/distributed/test_sequence_parallel.py
+    - pytest -v -s tests/compile/test_async_tp.py
+    - pytest -v -s tests/compile/test_sequence_parallelism.py
+    - pytest -v -s tests/compile/test_fusion_all_reduce.py
+    - "pytest -v -s tests/compile/test_fusions_e2e.py -k 'not Llama-4'"
+    - pytest -v -s tests/distributed/test_sequence_parallel.py
    - pytest -v -s tests/distributed/test_context_parallel.py
-    - CUDA_VISIBLE_DEVICES=1,2 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
+    - CUDA_VISIBLE_DEVICES=1,2 VLLM_ALL2ALL_BACKEND=deepep_high_throughput VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1  --dp-size=2 --max-model-len 2048
    - pytest -v -s tests/v1/distributed/test_dbo.py

- label: LM Eval Large Models (H200) # optional
-  timeout_in_minutes: 60
-  gpu: h200
-  optional: true
-  num_gpus: 8
-  commands:
-    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-h200.txt
-
 ##### B200 test #####
 - label: Distributed Tests (B200) # optional
  gpu: b200
@@ -1383,14 +1288,12 @@ steps:
 - label: Prime-RL Integration Test # 15min
  timeout_in_minutes: 30
  optional: true
-  soft_fail: true
  num_gpus: 2
  working_dir: "/vllm-workspace"
  source_file_dependencies:
  - vllm/
  - .buildkite/scripts/run-prime-rl-test.sh
  commands:
-    - nvidia-smi
    - bash .buildkite/scripts/run-prime-rl-test.sh

 - label: DeepSeek V2-Lite Accuracy
@@ -1402,43 +1305,11 @@ steps:
  commands:
  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010

- label: Qwen3-30B-A3B-FP8-block Accuracy (H100)
+- label: Qwen3-30B-A3B-FP8-block Accuracy
  timeout_in_minutes: 60
  gpu: h100
  optional: true
  num_gpus: 4
  working_dir: "/vllm-workspace"
  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
-
- label: Qwen3-30B-A3B-FP8-block Accuracy (B200)
-  timeout_in_minutes: 60
-  gpu: b200
-  optional: true
-  num_gpus: 2
-  working_dir: "/vllm-workspace"
-  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
-
-##### MoE Refactor (Temporary) Tests #####
-
- label: MoE Refactor Integration Test (H100 - TEMPORARY) # optional
-  gpu: h100
-  optional: true
-  num_gpus: 2
-  commands:
-    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-h100.txt
-  
- label: MoE Refactor Integration Test (B200 - TEMPORARY) # optional
-  gpu: b200
-  optional: true
-  num_gpus: 2
-  commands:
-    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-b200.txt
-
- label: MoE Refactor Integration Test (B200 DP - TEMPORARY) # optional
-  gpu: b200
-  optional: true
-  num_gpus: 2
-  commands:
-    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor-dp-ep/config-b200.txt
+  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep.sh 0.8 200 8020
--- a/.buildkite/test_areas/attention.yaml
+++ b/.buildkite/test_areas/attention.yaml
@@ -1,21 +0,0 @@
-group: Attention
-depends_on: 
-  - image-build
-steps:
- label: V1 attention (H100)
-  timeout_in_minutes: 30
-  gpu: h100
-  source_file_dependencies:
-    - vllm/v1/attention
-    - tests/v1/attention
-  commands:
-    - pytest -v -s v1/attention
-
- label: V1 attention (B200)
-  timeout_in_minutes: 30
-  gpu: b200
-  source_file_dependencies:
-    - vllm/v1/attention
-    - tests/v1/attention
-  commands:
-    - VLLM_DISABLE_FLASHINFER_PREFILL=1 pytest -v -s v1/attention # TODO: FI prefill is bugged and causes incorrectness, fix this
--- a/.buildkite/test_areas/basic_correctness.yaml
+++ b/.buildkite/test_areas/basic_correctness.yaml
@@ -1,16 +0,0 @@
-group: Basic Correctness
-depends_on: 
-  - image-build
-steps:
- label: Basic Correctness
-  timeout_in_minutes: 30
-  source_file_dependencies:
-  - vllm/
-  - tests/basic_correctness/test_basic_correctness
-  - tests/basic_correctness/test_cpu_offload
-  - tests/basic_correctness/test_cumem.py
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s basic_correctness/test_cumem.py
-  - pytest -v -s basic_correctness/test_basic_correctness.py
-  - pytest -v -s basic_correctness/test_cpu_offload.py
--- a/.buildkite/test_areas/benchmarks.yaml
+++ b/.buildkite/test_areas/benchmarks.yaml
@@ -1,19 +0,0 @@
-group: Benchmarks
-depends_on: 
-  - image-build
-steps:
- label: Benchmarks
-  timeout_in_minutes: 20
-  working_dir: "/vllm-workspace/.buildkite"
-  source_file_dependencies:
-  - benchmarks/
-  commands:
-  - bash scripts/run-benchmarks.sh
-
- label: Benchmarks CLI Test
-  timeout_in_minutes: 20
-  source_file_dependencies:
-  - vllm/
-  - tests/benchmarks/
-  commands:
-  - pytest -v -s benchmarks/
--- a/.buildkite/test_areas/compile.yaml
+++ b/.buildkite/test_areas/compile.yaml
@@ -1,57 +0,0 @@
-group: Compile
-depends_on: 
-  - image-build
-steps:
- label: Fusion and Compile Tests (B200)
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/v1/worker/
-  - vllm/v1/cudagraph_dispatcher.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - tests/compile/test_fusion_attn.py
-  - tests/compile/test_silu_mul_quant_fusion.py
-  - tests/compile/distributed/test_fusion_all_reduce.py
-  - tests/compile/distributed/test_fusions_e2e.py
-  - tests/compile/fullgraph/test_full_graph.py
-  commands:
-    - nvidia-smi
-    - pytest -v -s tests/compile/test_fusion_attn.py
-    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
-    # this runner has 2 GPUs available even though num_gpus=2 is not set
-    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
-    # Limit to Inductor partition, no custom ops, and allreduce & attn fusion to reduce running time
-    # Wrap with quotes to escape yaml
-    - "pytest -v -s tests/compile/distributed/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm -k 'True and not +quant_fp8 and not +rms_norm'"
-    # test_fp8_kv_scale_compile requires FlashAttention (not supported on default L4/L40)
-    - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
-
- label: Fusion E2E (2 GPUs)(B200)
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  optional: true
-  num_gpus: 2
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - tests/compile/distributed/test_fusions_e2e.py
-  commands:
-    - nvidia-smi
-    # Run all e2e fusion tests
-    - pytest -v -s tests/compile/distributed/test_fusions_e2e.py
-
--- a/.buildkite/test_areas/cuda.yaml
+++ b/.buildkite/test_areas/cuda.yaml
@@ -1,22 +0,0 @@
-group: CUDA
-depends_on: 
-  - image-build
-steps:
- label: Platform Tests (CUDA)
-  timeout_in_minutes: 15
-  source_file_dependencies:
-  - vllm/
-  - tests/cuda
-  commands:
-    - pytest -v -s cuda/test_cuda_context.py
-
- label: Cudagraph
-  timeout_in_minutes: 20
-  source_file_dependencies:
-  - tests/v1/cudagraph
-  - vllm/v1/cudagraph_dispatcher.py
-  - vllm/config/compilation.py
-  - vllm/compilation
-  commands:
-    - pytest -v -s v1/cudagraph/test_cudagraph_dispatch.py
-    - pytest -v -s v1/cudagraph/test_cudagraph_mode.py
--- a/.buildkite/test_areas/distributed.yaml
+++ b/.buildkite/test_areas/distributed.yaml
@@ -1,199 +0,0 @@
-group: Distributed
-depends_on: 
-  - image-build
-steps:
- label: Distributed Comm Ops
-  timeout_in_minutes: 20
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
-  source_file_dependencies:
-  - vllm/distributed
-  - tests/distributed
-  commands:
-  - pytest -v -s distributed/test_comm_ops.py
-  - pytest -v -s distributed/test_shm_broadcast.py
-  - pytest -v -s distributed/test_shm_buffer.py
-  - pytest -v -s distributed/test_shm_storage.py
-
- label: Distributed (2 GPUs)
-  timeout_in_minutes: 90
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
-  source_file_dependencies:
-  - vllm/compilation/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/worker/worker_base.py
-  - vllm/v1/engine/
-  - vllm/v1/worker/
-  - tests/compile/fullgraph/test_basic_correctness.py
-  - tests/compile/test_wrapper.py
-  - tests/distributed/
-  - tests/entrypoints/llm/test_collective_rpc.py
-  - tests/v1/distributed
-  - tests/v1/entrypoints/openai/test_multi_api_servers.py
-  - tests/v1/shutdown
-  - tests/v1/worker/test_worker_memory_snapshot.py
-  commands:
-  # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
-  - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
-  - pytest -v -s entrypoints/llm/test_collective_rpc.py
-  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
-  - pytest -v -s ./compile/test_wrapper.py
-  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - pytest -v -s distributed/test_sequence_parallel.py
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
-  - pytest -v -s v1/worker/test_worker_memory_snapshot.py
-
- label: Distributed Tests (4 GPUs)
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-  - vllm/distributed/
-  - tests/distributed/test_utils
-  - tests/distributed/test_pynccl
-  - tests/distributed/test_events
-  - tests/compile/fullgraph/test_basic_correctness.py
-  - examples/offline_inference/rlhf.py
-  - examples/offline_inference/rlhf_colocate.py
-  - tests/examples/offline_inference/data_parallel.py
-  - tests/v1/distributed
-  - tests/v1/engine/test_engine_core_client.py
-  - tests/distributed/test_symm_mem_allreduce.py
-  commands:
-  # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  # test with torchrun tp=2 and external_dp=2
-  - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
-  # test with torchrun tp=2 and pp=2
-  - PP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
-  # test with torchrun tp=4 and dp=1
-  - TP_SIZE=4 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
-  # test with torchrun tp=2, pp=2 and dp=1
-  - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
-  # test with torchrun tp=1 and dp=4 with ep
-  - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
-  # test with torchrun tp=2 and dp=2 with ep
-  - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
-  # test with internal dp
-  - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py
-  - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
-  - pytest -v -s distributed/test_utils.py
-  - pytest -v -s compile/fullgraph/test_basic_correctness.py
-  - pytest -v -s distributed/test_pynccl.py
-  - pytest -v -s distributed/test_events.py
-  - pytest -v -s distributed/test_symm_mem_allreduce.py
-  # TODO: create a dedicated test section for multi-GPU example tests
-  # when we have multiple distributed example tests
-  - cd ../examples/offline_inference
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf.py
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
-
- label: Distributed Tests (8 GPUs)(H100)
-  timeout_in_minutes: 10
-  gpu: h100
-  num_gpus: 8
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - examples/offline_inference/torchrun_dp_example.py
-  - vllm/config/parallel.py
-  - vllm/distributed/
-  - vllm/v1/engine/llm_engine.py
-  - vllm/v1/executor/uniproc_executor.py
-  - vllm/v1/worker/gpu_worker.py
-  commands:
-  # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  # test with torchrun tp=2 and dp=4 with ep
-  - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
-
- label: Distributed Tests (4 GPUs)(A100)
-  gpu: a100
-  optional: true
-  num_gpus: 4
-  source_file_dependencies:
-  - vllm/
-  commands:
-  # NOTE: don't test llama model here, it seems hf implementation is buggy
-  # see https://github.com/vllm-project/vllm/pull/5689 for details
-  - pytest -v -s distributed/test_custom_all_reduce.py
-  - torchrun --nproc_per_node=2 distributed/test_ca_buffer_sharing.py
-  - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
-  - pytest -v -s -x lora/test_mixtral.py
-
- label: Distributed Tests (2 GPUs)(H200)
-  gpu: h200
-  optional: true
-  working_dir: "/vllm-workspace/"
-  num_gpus: 2
-  commands:
-    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_async_tp.py
-    - pytest -v -s tests/compile/distributed/test_sequence_parallelism.py
-    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
-    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'
-    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/distributed/test_sequence_parallel.py
-    - pytest -v -s tests/distributed/test_context_parallel.py
-    - CUDA_VISIBLE_DEVICES=1,2 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
-    - pytest -v -s tests/v1/distributed/test_dbo.py
-
- label: Distributed Tests (2 GPUs)(B200)
-  gpu: b200
-  optional: true
-  working_dir: "/vllm-workspace/"
-  num_gpus: 2
-  commands:
-    - pytest -v -s tests/distributed/test_context_parallel.py
-    - pytest -v -s tests/distributed/test_nccl_symm_mem_allreduce.py
-    - pytest -v -s tests/v1/distributed/test_dbo.py
-
- label: 2 Node Test (4 GPUs)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
-  num_nodes: 2
-  source_file_dependencies:
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/model_executor/models/
-  - tests/distributed/
-  - tests/examples/offline_inference/data_parallel.py
-  commands:
-    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:0bec63fa317e1fbd62e19b0fc31c43c81bf89077 "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
-
- label: Distributed NixlConnector PD accuracy (4 GPUs)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
- label: Pipeline + Context Parallelism (4 GPUs))
-  timeout_in_minutes: 60
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/model_executor/models/
-  - tests/distributed/
-  commands:
-  - pytest -v -s distributed/test_pp_cudagraph.py
-  - pytest -v -s distributed/test_pipeline_parallel.py
--- a/.buildkite/test_areas/e2e_integration.yaml
+++ b/.buildkite/test_areas/e2e_integration.yaml
@@ -1,42 +0,0 @@
-group: E2E Integration
-depends_on: 
-  - image-build
-steps:
- label: DeepSeek V2-Lite Accuracy
-  timeout_in_minutes: 60
-  gpu: h100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace"
-  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010
-
- label: Qwen3-30B-A3B-FP8-block Accuracy
-  timeout_in_minutes: 60
-  gpu: h100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace"
-  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
-
- label: Qwen3-30B-A3B-FP8-block Accuracy (B200)
-  timeout_in_minutes: 60
-  gpu: b200
-  optional: true
-  num_gpus: 2
-  working_dir: "/vllm-workspace"
-  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
-
- label: Prime-RL Integration (2 GPUs)
-  timeout_in_minutes: 30
-  optional: true
-  soft_fail: true
-  num_gpus: 2
-  working_dir: "/vllm-workspace"
-  source_file_dependencies:
-  - vllm/
-  - .buildkite/scripts/run-prime-rl-test.sh
-  commands:
-    - bash .buildkite/scripts/run-prime-rl-test.sh
--- a/.buildkite/test_areas/engine.yaml
+++ b/.buildkite/test_areas/engine.yaml
@@ -1,26 +0,0 @@
-group: Engine
-depends_on: 
-  - image-build
-steps:
- label: Engine
-  timeout_in_minutes: 15
-  source_file_dependencies:
-  - vllm/
-  - tests/engine
-  - tests/test_sequence
-  - tests/test_config
-  - tests/test_logger
-  - tests/test_vllm_port
-  commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
-
- label: V1 e2e + engine
-  timeout_in_minutes: 45
-  source_file_dependencies:
-    - vllm/
-    - tests/v1
-  commands:
-    # TODO: accuracy does not match, whether setting
-    # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
-    - pytest -v -s v1/e2e
-    - pytest -v -s v1/engine
--- a/.buildkite/test_areas/entrypoints.yaml
+++ b/.buildkite/test_areas/entrypoints.yaml
@@ -1,90 +0,0 @@
-group: Entrypoints
-depends_on: 
-  - image-build
-steps:
- label: Entrypoints Unit Tests  
-  timeout_in_minutes: 10
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/entrypoints
-  - tests/entrypoints/
-  commands:
-  - pytest -v -s entrypoints/openai/tool_parsers
-  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
-
- label: Entrypoints Integration (LLM)
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/llm
-  - tests/entrypoints/offline_mode
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
-  - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
-  - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
-
- label: Entrypoints Integration (API Server 1)
-  timeout_in_minutes: 130
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/openai
-  - tests/entrypoints/test_chat_utils
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/  --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses
-  - pytest -v -s entrypoints/test_chat_utils.py
-
- label: Entrypoints Integration (API Server 2)
-  timeout_in_minutes: 130
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/tool_use
-  - tests/entrypoints/sleep
-  - tests/entrypoints/instrumentator
-  - tests/entrypoints/rpc
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/rpc
-  - pytest -v -s entrypoints/instrumentator
-  - pytest -v -s entrypoints/sleep
-  - pytest -v -s tool_use
-
- label: Entrypoints Integration (Pooling)
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/pooling
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/pooling
-
- label: Entrypoints Integration (Responses API)
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/openai/responses
-  commands:
-  - pytest -v -s entrypoints/openai/responses
-
- label: Entrypoints V1
-  timeout_in_minutes: 50
-  source_file_dependencies:
-    - vllm/
-    - tests/v1
-  commands:
-    - pytest -v -s v1/entrypoints
-
- label: OpenAI API Correctness
-  timeout_in_minutes: 30
-  source_file_dependencies:
-  - csrc/
-  - vllm/entrypoints/openai/
-  - vllm/model_executor/models/whisper.py
-  commands: # LMEval+Transcription WER check
-  - pytest -s entrypoints/openai/correctness/
--- a/.buildkite/test_areas/expert_parallelism.yaml
+++ b/.buildkite/test_areas/expert_parallelism.yaml
@@ -1,23 +0,0 @@
-group: Expert Parallelism
-depends_on: 
-  - image-build
-steps:
- label: EPLB Algorithm
-  timeout_in_minutes: 15
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/eplb
-  - tests/distributed/test_eplb_algo.py
-  commands:
-  - pytest -v -s distributed/test_eplb_algo.py
-
- label: EPLB Execution
-  timeout_in_minutes: 20
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-  - vllm/distributed/eplb
-  - tests/distributed/test_eplb_execute.py
-  commands:
-  - pytest -v -s distributed/test_eplb_execute.py
-  - pytest -v -s distributed/test_eplb_spec_decode.py
--- a/.buildkite/test_areas/kernels.yaml
+++ b/.buildkite/test_areas/kernels.yaml
@@ -1,117 +0,0 @@
-group: Kernels
-depends_on: 
-  - image-build
-steps:
- label: Kernels Core Operation Test
-  timeout_in_minutes: 75
-  source_file_dependencies:
-  - csrc/
-  - tests/kernels/core
-  - tests/kernels/test_top_k_per_row.py
-  commands:
-    - pytest -v -s kernels/core kernels/test_top_k_per_row.py
-
- label: Kernels Attention Test %N
-  timeout_in_minutes: 35
-  source_file_dependencies:
-  - csrc/attention/
-  - vllm/attention
-  - vllm/v1/attention
-  - tests/kernels/attention
-  commands:
-    - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 2
-
- label: Kernels Quantization Test %N
-  timeout_in_minutes: 90
-  source_file_dependencies:
-  - csrc/quantization/
-  - vllm/model_executor/layers/quantization
-  - tests/kernels/quantization
-  commands:
-    - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 2
-
- label: Kernels MoE Test %N
-  timeout_in_minutes: 60
-  source_file_dependencies:
-  - csrc/quantization/cutlass_w8a8/moe/
-  - csrc/moe/
-  - tests/kernels/moe
-  - vllm/model_executor/layers/fused_moe/
-  - vllm/distributed/device_communicators/
-  - vllm/envs.py
-  - vllm/config
-  commands:
-    - pytest -v -s kernels/moe --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 2
-
- label: Kernels Mamba Test
-  timeout_in_minutes: 45
-  source_file_dependencies:
-  - csrc/mamba/
-  - tests/kernels/mamba
-  - vllm/model_executor/layers/mamba/ops
-  commands:
-    - pytest -v -s kernels/mamba
-
- label: Kernels DeepGEMM Test (H100)
-  timeout_in_minutes: 45
-  gpu: h100
-  num_gpus: 1
-  source_file_dependencies:
-  - tools/install_deepgemm.sh
-  - vllm/utils/deep_gemm.py
-  - vllm/model_executor/layers/fused_moe
-  - vllm/model_executor/layers/quantization
-  - tests/kernels/quantization/test_block_fp8.py
-  - tests/kernels/moe/test_deepgemm.py
-  - tests/kernels/moe/test_batched_deepgemm.py
-  - tests/kernels/attention/test_deepgemm_attention.py
-  commands:
-    - pytest -v -s kernels/quantization/test_block_fp8.py -k deep_gemm
-    - pytest -v -s kernels/moe/test_deepgemm.py
-    - pytest -v -s kernels/moe/test_batched_deepgemm.py
-    - pytest -v -s kernels/attention/test_deepgemm_attention.py
-
- label: Kernels (B200)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  # optional: true
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - csrc/attention/mla/
-  - csrc/quantization/cutlass_w8a8/moe/
-  - vllm/model_executor/layers/fused_moe/cutlass_moe.py
-  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py
-  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/v1/attention/backends/mla/cutlass_mla.py
-  - vllm/v1/attention/backends/mla/flashinfer_mla.py
-  - vllm/v1/attention/selector.py
-  - vllm/platforms/cuda.py
-  commands:
-    - nvidia-smi
-    - python3 examples/offline_inference/basic/chat.py
-    # Attention
-    # num_heads2 broken by https://github.com/flashinfer-ai/flashinfer/issues/1353
-    - pytest -v -s tests/kernels/attention/test_attention_selector.py
-    - pytest -v -s tests/kernels/attention/test_flashinfer.py -k 'not num_heads2'
-    - pytest -v -s tests/kernels/attention/test_flashinfer_trtllm_attention.py
-    - pytest -v -s tests/kernels/attention/test_cutlass_mla_decode.py
-    - pytest -v -s tests/kernels/attention/test_flashinfer_mla_decode.py
-    # Quantization
-    - pytest -v -s tests/kernels/quantization/test_cutlass_scaled_mm.py -k 'fp8'
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_quant.py
-    - pytest -v -s tests/kernels/quantization/test_silu_mul_nvfp4_quant.py
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
-    - pytest -v -s tests/kernels/quantization/test_flashinfer_scaled_mm.py
-    - pytest -v -s tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
-    - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.py
-    - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
-    - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
-    - pytest -v -s tests/kernels/moe/test_flashinfer.py
-    - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py
--- a/.buildkite/test_areas/lm_eval.yaml
+++ b/.buildkite/test_areas/lm_eval.yaml
@@ -1,46 +0,0 @@
-group: LM Eval
-depends_on: 
-  - image-build
-steps:
- label: LM Eval Small Models
-  timeout_in_minutes: 75
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  autorun_on_main: true
-  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt
-
- label: LM Eval Large Models (4 GPUs)(A100)
-  gpu: a100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
-
- label: LM Eval Large Models (4 GPUs)(H100)
-  gpu: h100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  commands:
-    - export VLLM_USE_DEEP_GEMM=0  # We found Triton is faster than DeepGEMM for H100
-    - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
-
- label: LM Eval Small Models (B200)
-  timeout_in_minutes: 120
-  gpu: b200
-  optional: true
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt
--- a/.buildkite/test_areas/lora.yaml
+++ b/.buildkite/test_areas/lora.yaml
@@ -1,33 +0,0 @@
-group: LoRA
-depends_on: 
-  - image-build
-steps:
- label: LoRA %N
-  timeout_in_minutes: 30
-  source_file_dependencies:
-  - vllm/lora
-  - tests/lora
-  commands:
-    - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py
-  parallelism: 4
-
-
- label: LoRA TP (Distributed)
-  timeout_in_minutes: 30
-  num_gpus: 4
-  source_file_dependencies:
-  - vllm/lora
-  - tests/lora
-  commands:
-    # FIXIT: find out which code initialize cuda before running the test
-    # before the fix, we need to use spawn to test it
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    # Alot of these tests are on the edge of OOMing
-    - export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
-    # There is some Tensor Parallelism related processing logic in LoRA that
-    # requires multi-GPU testing for validation.
-    - pytest -v -s -x lora/test_chatglm3_tp.py
-    - pytest -v -s -x lora/test_llama_tp.py
-    - pytest -v -s -x lora/test_llm_with_multi_loras.py
-    - pytest -v -s -x lora/test_olmoe_tp.py
-    - pytest -v -s -x lora/test_gptoss_tp.py
--- a/.buildkite/test_areas/misc.yaml
+++ b/.buildkite/test_areas/misc.yaml
@@ -1,165 +0,0 @@
-group: Miscellaneous
-depends_on: 
-  - image-build
-steps:
- label: V1 Others
-  timeout_in_minutes: 60
-  source_file_dependencies:
-    - vllm/
-    - tests/v1
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    # split the test to avoid interference
-    - pytest -v -s -m 'not cpu_test' v1/core
-    - pytest -v -s v1/executor
-    - pytest -v -s v1/kv_offload
-    - pytest -v -s v1/sample
-    - pytest -v -s v1/logits_processors
-    - pytest -v -s v1/worker
-    - pytest -v -s v1/spec_decode
-    - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
-    - pytest -v -s -m 'not cpu_test' v1/metrics
-    - pytest -v -s v1/test_oracle.py
-    - pytest -v -s v1/test_request.py
-    - pytest -v -s v1/test_outputs.py
-    # Integration test for streaming correctness (requires special branch).
-    - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
-    - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
-
- label: V1 Others (CPU)
-  depends_on: ~
-  source_file_dependencies:
-    - vllm/
-    - tests/v1
-  no_gpu: true
-  commands:
-    # split the test to avoid interference
-    - pytest -v -s -m 'cpu_test' v1/core
-    - pytest -v -s v1/structured_output
-    - pytest -v -s v1/test_serial_utils.py
-    - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
-    - pytest -v -s -m 'cpu_test' v1/metrics
-
- label: Regression
-  timeout_in_minutes: 20
-  source_file_dependencies:
-  - vllm/
-  - tests/test_regression
-  commands:
-  - pip install modelscope
-  - pytest -v -s test_regression.py
-  working_dir: "/vllm-workspace/tests" # optional
-
- label: Examples
-  timeout_in_minutes: 45
-  working_dir: "/vllm-workspace/examples"
-  source_file_dependencies:
-  - vllm/entrypoints
-  - vllm/multimodal
-  - examples/
-  commands:
-    - pip install tensorizer # for tensorizer test
-    - python3 offline_inference/basic/chat.py # for basic
-    - python3 offline_inference/basic/generate.py --model facebook/opt-125m
-    - python3 offline_inference/basic/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
-    - python3 offline_inference/basic/classify.py
-    - python3 offline_inference/basic/embed.py
-    - python3 offline_inference/basic/score.py
-    # for multi-modal models
-    - python3 offline_inference/audio_language.py --seed 0
-    - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
-     # for pooling models
-    - python3 pooling/pooling/vision_language_pooling.py --seed 0
-    # for features demo
-    - python3 offline_inference/prefix_caching.py
-    - python3 offline_inference/llm_engine_example.py
-    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
-
- label: Metrics, Tracing (2 GPUs)
-  timeout_in_minutes: 20
-  num_gpus: 2
-  source_file_dependencies:
-  - vllm/
-  - tests/v1/tracing
-  commands:
-  - "pip install \
-      'opentelemetry-sdk>=1.26.0' \
-      'opentelemetry-api>=1.26.0' \
-      'opentelemetry-exporter-otlp>=1.26.0' \
-      'opentelemetry-semantic-conventions-ai>=0.4.1'"
-  - pytest -v -s v1/tracing
-
- label: Python-only Installation
-  depends_on: ~
-  timeout_in_minutes: 20
-  source_file_dependencies:
-  - tests/standalone_tests/python_only_compile.sh
-  - setup.py
-  commands:
-  - bash standalone_tests/python_only_compile.sh
-
- label: Async Engine, Inputs, Utils, Worker
-  timeout_in_minutes: 50
-  source_file_dependencies:
-  - vllm/
-  - tests/multimodal
-  - tests/utils_
-  commands:
-  - pytest -v -s -m 'not cpu_test' multimodal
-  - pytest -v -s utils_
-
- label: Async Engine, Inputs, Utils, Worker, Config (CPU)
-  depends_on: ~
-  timeout_in_minutes: 30
-  source_file_dependencies:
-  - vllm/
-  - tests/test_inputs.py
-  - tests/test_outputs.py
-  - tests/multimodal
-  - tests/standalone_tests/lazy_imports.py
-  - tests/tokenizers_
-  - tests/tool_parsers
-  - tests/transformers_utils
-  - tests/config
-  no_gpu: true
-  commands:
-  - python3 standalone_tests/lazy_imports.py
-  - pytest -v -s test_inputs.py
-  - pytest -v -s test_outputs.py
-  - pytest -v -s -m 'cpu_test' multimodal
-  - pytest -v -s tokenizers_
-  - pytest -v -s tool_parsers
-  - pytest -v -s transformers_utils
-  - pytest -v -s config
-
- label: GPT-OSS Eval (B200)
-  timeout_in_minutes: 60
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  optional: true
-  source_file_dependencies:
-  - tests/evals/gpt_oss
-  - vllm/model_executor/models/gpt_oss.py
-  - vllm/model_executor/layers/quantization/mxfp4.py
-  - vllm/v1/attention/backends/flashinfer.py
-  commands:
-    - uv pip install --system 'gpt-oss[eval]==0.0.5'
-    - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58
-
- label: Batch Invariance (H100)
-  timeout_in_minutes: 25
-  gpu: h100
-  source_file_dependencies:
-    - vllm/v1/attention
-    - vllm/model_executor/layers
-    - tests/v1/determinism/
-  commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pip install pytest-timeout pytest-forked
-    - pytest -v -s v1/determinism/test_batch_invariance.py
-    - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
--- a/.buildkite/test_areas/model_executor.yaml
+++ b/.buildkite/test_areas/model_executor.yaml
@@ -1,17 +0,0 @@
-group: Model Executor
-depends_on: 
-  - image-build
-steps:
- label: Model Executor
-  timeout_in_minutes: 35
-  source_file_dependencies:
-  - vllm/engine/arg_utils.py
-  - vllm/config/model.py
-  - vllm/model_executor
-  - tests/model_executor
-  - tests/entrypoints/openai/test_tensorizer_entrypoint.py
-  commands:
-    - apt-get update && apt-get install -y curl libsodium23
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s model_executor
-    - pytest -v -s entrypoints/openai/test_tensorizer_entrypoint.py
--- a/.buildkite/test_areas/models_basic.yaml
+++ b/.buildkite/test_areas/models_basic.yaml
@@ -1,64 +0,0 @@
-group: Models - Basic
-depends_on: 
-  - image-build
-steps:
- label: Basic Models Tests (Initialization)
-  timeout_in_minutes: 45
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/test_initialization.py
-  - tests/models/registry.py
-  commands:
-    # Run a subset of model initialization tests
-    - pytest -v -s models/test_initialization.py::test_can_initialize_small_subset
-
- label: Basic Models Tests (Extra Initialization) %N
-  timeout_in_minutes: 45
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/model_executor/models/
-  - tests/models/test_initialization.py
-  - tests/models/registry.py
-  commands:
-    # Only when vLLM model source is modified - test initialization of a large
-    # subset of supported models (the complement of the small subset in the above
-    # test.) Also run if model initialization test file is modified
-    - pytest -v -s models/test_initialization.py -k 'not test_can_initialize_small_subset' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
-  parallelism: 2
-
- label: Basic Models Tests (Other)
-  timeout_in_minutes: 45
-  source_file_dependencies:
-  - vllm/
-  - tests/models/test_transformers.py
-  - tests/models/test_registry.py
-  commands:
-    - pytest -v -s models/test_transformers.py models/test_registry.py
-
- label: Basic Models Test (Other CPU) # 5min
-  timeout_in_minutes: 10
-  source_file_dependencies:
-  - vllm/
-  - tests/models/test_utils.py
-  - tests/models/test_vision.py
-  no_gpu: true
-  commands:
-    - pytest -v -s models/test_utils.py models/test_vision.py
-
- label: Transformers Nightly Models
-  working_dir: "/vllm-workspace/"
-  optional: true
-  soft_fail: true
-  commands:
-    - pip install --upgrade git+https://github.com/huggingface/transformers
-    - pytest -v -s tests/models/test_initialization.py
-    - pytest -v -s tests/models/test_transformers.py
-    - pytest -v -s tests/models/multimodal/processing/
-    - pytest -v -s tests/models/multimodal/test_mapping.py
-    - python3 examples/offline_inference/basic/chat.py
-    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
-    # Whisper needs spawn method to avoid deadlock
-    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
--- a/.buildkite/test_areas/models_distributed.yaml
+++ b/.buildkite/test_areas/models_distributed.yaml
@@ -1,22 +0,0 @@
-group: Models - Distributed
-depends_on: 
-  - image-build
-steps:
- label: Distributed Model Tests (2 GPUs)
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
-  source_file_dependencies:
-  - vllm/model_executor/model_loader/sharded_state_loader.py
-  - vllm/model_executor/models/
-  - tests/basic_correctness/
-  - tests/model_executor/model_loader/test_sharded_state_loader.py
-  - tests/models/
-  commands:
-  - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py
-  # Avoid importing model tests that cause CUDA reinitialization error
-  - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/language -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py
-  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)'
--- a/.buildkite/test_areas/models_language.yaml
+++ b/.buildkite/test_areas/models_language.yaml
@@ -1,91 +0,0 @@
-group: Models - Language
-depends_on: 
-  - image-build
-steps:
- label: Language Models Tests (Standard)
-  timeout_in_minutes: 25
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/language
-  commands:
-    # Test standard language models, excluding a subset of slow tests
-    - pip freeze | grep -E 'torch'
-    - pytest -v -s models/language -m 'core_model and (not slow_test)'
-
- label: Language Models Tests (Extra Standard) %N
-  timeout_in_minutes: 45
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/model_executor/models/
-  - tests/models/language/pooling/test_embedding.py
-  - tests/models/language/generation/test_common.py
-  - tests/models/language/pooling/test_classification.py
-  commands:
-    # Shard slow subset of standard language models tests. Only run when model
-    # source is modified, or when specified test files are modified
-    - pip freeze | grep -E 'torch'
-    - pytest -v -s models/language -m 'core_model and slow_test' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
-  parallelism: 2
-
- label: Language Models Tests (Hybrid) %N
-  timeout_in_minutes: 75
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/language/generation
-  commands:
-    # Install fast path packages for testing against transformers
-    # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
-    # Shard hybrid language model tests
-    - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
-  parallelism: 2
-
- label: Language Models Test (Extended Generation) # 80min
-  timeout_in_minutes: 110
-  mirror_hardwares: [amdexperimental]
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/language/generation
-  commands:
-    # Install fast path packages for testing against transformers
-    # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
-    - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
-
- label: Language Models Test (PPL)
-  timeout_in_minutes: 110
-  mirror_hardwares: [amdexperimental]
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/language/generation_ppl_test
-  commands:
-    - pytest -v -s models/language/generation_ppl_test
-
- label: Language Models Test (Extended Pooling)  # 36min
-  timeout_in_minutes: 50
-  mirror_hardwares: [amdexperimental]
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/language/pooling
-  commands:
-    - pytest -v -s models/language/pooling -m 'not core_model'
-
- label: Language Models Test (MTEB)
-  timeout_in_minutes: 110
-  mirror_hardwares: [amdexperimental]
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/language/pooling_mteb_test
-  commands:
-    - pytest -v -s models/language/pooling_mteb_test
--- a/.buildkite/test_areas/models_multimodal.yaml
+++ b/.buildkite/test_areas/models_multimodal.yaml
@@ -1,79 +0,0 @@
-group: Models - Multimodal
-depends_on: 
-  - image-build
-steps:
- label: Multi-Modal Models (Standard) # 60min
-  timeout_in_minutes: 80
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  commands:
-    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pip freeze | grep -E 'torch'
-    - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing
-    - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
-
- label: Multi-Modal Processor Test (CPU)
-  timeout_in_minutes: 60
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  no_gpu: true
-  commands:
-    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py
-
- label: Multi-Modal Processor # 44min
-  timeout_in_minutes: 60
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  commands:
-    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/multimodal/processing/test_tensor_schema.py
-
- label: Multi-Modal Accuracy Eval (Small Models) # 50min
-  timeout_in_minutes: 70
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - vllm/multimodal/
-  - vllm/inputs/
-  - vllm/v1/core/
-  commands:
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
-
- label: Multi-Modal Models (Extended) 1
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  commands:
-    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/multimodal -m 'not core_model' --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/processing
-
- label: Multi-Modal Models (Extended) 2
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  commands:
-    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
-
- label: Multi-Modal Models (Extended) 3
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  commands:
-    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
-
-# This test is used only in PR development phase to test individual models and should never run on main
- label: Custom Models
-  optional: true
-  commands:
-    - echo 'Testing custom models...'
-    # PR authors can temporarily add commands below to test individual models
-    # e.g. pytest -v -s models/encoder_decoder/vision_language/test_mllama.py
-    # *To avoid merge conflicts, remember to REMOVE (not just comment out) them before merging the PR*
--- a/.buildkite/test_areas/plugins.yaml
+++ b/.buildkite/test_areas/plugins.yaml
@@ -1,34 +0,0 @@
-group: Plugins
-depends_on: 
-  - image-build
-steps:
- label: Plugin Tests (2 GPUs)
-  timeout_in_minutes: 60
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
-  source_file_dependencies:
-  - vllm/plugins/
-  - tests/plugins/
-  commands:
-  # begin platform plugin and general plugin tests, all the code in-between runs on dummy platform
-  - pip install -e ./plugins/vllm_add_dummy_platform
-  - pytest -v -s plugins_tests/test_platform_plugins.py
-  - pip uninstall vllm_add_dummy_platform -y
-  # end platform plugin tests
-  # begin io_processor plugins test, all the code in between uses the prithvi_io_processor plugin
-  - pip install -e ./plugins/prithvi_io_processor_plugin
-  - pytest -v -s plugins_tests/test_io_processor_plugins.py
-  - pip uninstall prithvi_io_processor_plugin -y
-  # end io_processor plugins test
-  # begin stat_logger plugins test
-  - pip install -e ./plugins/vllm_add_dummy_stat_logger
-  - pytest -v -s plugins_tests/test_stats_logger_plugins.py
-  - pip uninstall dummy_stat_logger -y
-  # end stat_logger plugins test
-  # other tests continue here:
-  - pytest -v -s plugins_tests/test_scheduler_plugins.py
-  - pip install -e ./plugins/vllm_add_dummy_model
-  - pytest -v -s distributed/test_distributed_oot.py
-  - pytest -v -s entrypoints/openai/test_oot_registration.py # it needs a clean process
-  - pytest -v -s models/test_oot_registration.py # it needs a clean process
-  - pytest -v -s plugins/lora_resolvers # unit tests for in-tree lora resolver plugins
--- a/.buildkite/test_areas/pytorch.yaml
+++ b/.buildkite/test_areas/pytorch.yaml
@@ -1,52 +0,0 @@
-group: PyTorch
-depends_on: 
-  - image-build
-steps:
- label: PyTorch Compilation Unit Tests
-  timeout_in_minutes: 30
-  source_file_dependencies:
-    - vllm/
-    - tests/compile
-  commands:
-  # Run unit tests defined directly under compile/,
-  # not including subdirectories, which are usually heavier
-  # tests covered elsewhere.
-  # Use `find` to launch multiple instances of pytest so that
-  # they do not suffer from https://github.com/vllm-project/vllm/issues/28965
-  # However, find does not normally propagate error codes, so we combine it with xargs
-  # (using -0 for proper path handling)
-  - "find compile/ -maxdepth 1 -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
-
- label: PyTorch Fullgraph Smoke Test
-  timeout_in_minutes: 30
-  source_file_dependencies:
-  - vllm/
-  - tests/compile
-  commands:
-  # Run smoke tests under fullgraph directory, except test_full_graph.py
-  # as it is a heavy test that is covered in other steps.
-  # Use `find` to launch multiple instances of pytest so that
-  # they do not suffer from https://github.com/vllm-project/vllm/issues/28965
-  - "find compile/fullgraph/ -name 'test_*.py' -not -name 'test_full_graph.py' -exec pytest -s -v {} \\;"
-
- label: PyTorch Fullgraph
-  timeout_in_minutes: 40
-  source_file_dependencies:
-  - vllm/
-  - tests/compile
-  commands:
-    # fp8 kv scales not supported on sm89, tested on Blackwell instead
-  - pytest -v -s compile/fullgraph/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
-    # Limit to no custom ops to reduce running time
-    # Wrap with quotes to escape yaml and avoid starting -k string with a -
-  - "pytest -v -s compile/distributed/test_fusions_e2e.py -k 'TRITON and not +quant_fp8 and not Llama-4'"
-
- label: Pytorch Nightly Dependency Override Check # 2min
-  # if this test fails, it means the nightly torch version is not compatible with some
-  # of the dependencies. Please check the error message and add the package to whitelist
-  # in /vllm/tools/pre_commit/generate_nightly_torch_test.py
-  soft_fail: true
-  source_file_dependencies:
-  - requirements/nightly_torch_test.txt
-  commands:
-  - bash standalone_tests/pytorch_nightly_dependency.sh
--- a/.buildkite/test_areas/quantization.yaml
+++ b/.buildkite/test_areas/quantization.yaml
@@ -1,46 +0,0 @@
-group: Quantization
-depends_on: 
-  - image-build
-steps:
- label: Quantization
-  timeout_in_minutes: 90
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - tests/quantization
-  commands:
-  # temporary install here since we need nightly, will move to requirements/test.in
-  # after torchao 0.12 release, and pin a working version of torchao nightly here
-
-  # since torchao nightly is only compatible with torch nightly currently
-  # https://github.com/pytorch/ao/issues/2919, we'll have to skip new torchao tests for now
-  # we can only upgrade after this is resolved
-  # TODO(jerryzh168): resolve the above comment
-  - uv pip install --system torchao==0.13.0 --index-url https://download.pytorch.org/whl/cu129
-  - uv pip install --system conch-triton-kernels
-  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
-
- label: Quantized MoE Test (B200)
-  timeout_in_minutes: 60
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  source_file_dependencies:
-  - tests/quantization/test_blackwell_moe.py
-  - vllm/model_executor/models/deepseek_v2.py
-  - vllm/model_executor/models/gpt_oss.py
-  - vllm/model_executor/models/llama4.py
-  - vllm/model_executor/layers/fused_moe
-  - vllm/model_executor/layers/quantization/compressed_tensors
-  - vllm/model_executor/layers/quantization/modelopt.py
-  - vllm/model_executor/layers/quantization/mxfp4.py
-  - vllm/v1/attention/backends/flashinfer.py
-  commands:
-    - pytest -s -v tests/quantization/test_blackwell_moe.py
-
- label: Quantized Models Test
-  timeout_in_minutes: 60
-  source_file_dependencies:
-  - vllm/model_executor/layers/quantization
-  - tests/models/quantization
-  commands:
-    - pytest -v -s models/quantization
--- a/.buildkite/test_areas/samplers.yaml
+++ b/.buildkite/test_areas/samplers.yaml
@@ -1,14 +0,0 @@
-group: Samplers
-depends_on: 
-  - image-build
-steps:
- label: Samplers Test
-  timeout_in_minutes: 75
-  source_file_dependencies:
-  - vllm/model_executor/layers
-  - vllm/sampling_metadata.py
-  - tests/samplers
-  - tests/conftest.py
-  commands:
-    - pytest -v -s samplers
-    - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
--- a/.buildkite/test_areas/weight_loading.yaml
+++ b/.buildkite/test_areas/weight_loading.yaml
@@ -1,25 +0,0 @@
-group: Weight Loading
-depends_on: 
-  - image-build
-steps:
- label: Weight Loading Multiple GPU  # 33min
-  timeout_in_minutes: 45
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/weight_loading
-  commands:
-    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models.txt
-
- label: Weight Loading Multiple GPU - Large Models # optional
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
-  gpu: a100
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/weight_loading
-  commands:
-    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -3,18 +3,17 @@

 # This lists cover the "core" components of vLLM that require careful review
 /vllm/attention @LucasWilkinson
+/vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @njhill
 /vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @njhill @22quinn
 /vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
 /vllm/model_executor/layers/mamba @tdoublep
 /vllm/model_executor/model_loader @22quinn
-/vllm/model_executor/layers/batch_invariant.py @yewentao256 
 /vllm/multimodal @DarkLight1337 @ywang96 @NickLucche @tjtanaa
 /vllm/vllm_flash_attn @LucasWilkinson
 /vllm/lora @jeejeelee
 /vllm/reasoning @aarnphm @chaunceyjiang
 /vllm/entrypoints @aarnphm @chaunceyjiang
-/vllm/tool_parsers @aarnphm @chaunceyjiang
 /vllm/compilation @zou3519 @youkaichao @ProExpertProg
 /vllm/distributed/kv_transfer @NickLucche @ApostaC
 CMakeLists.txt @tlrmchlsmth @LucasWilkinson
@@ -26,7 +25,6 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson

 # vLLM V1
 /vllm/v1/attention @LucasWilkinson
-/vllm/v1/attention/backend.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @njhill
 /vllm/v1/attention/backends/mla @pavanimajety
 /vllm/v1/attention/backends/flashinfer.py @mgoin @pavanimajety
 /vllm/v1/attention/backends/triton_attn.py @tdoublep
@@ -37,9 +35,6 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /vllm/v1/kv_cache_interface.py @heheda12345
 /vllm/v1/offloading @ApostaC

-# Model runner V2
-/vllm/v1/worker/gpu @WoosukKwon
-
 # Test ownership
 /.buildkite/lm-eval-harness @mgoin 
 /tests/distributed/test_multi_node_assignment.py @youkaichao
@@ -61,7 +56,6 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/v1/kv_connector/nixl_integration @NickLucche
 /tests/v1/kv_connector @ApostaC
 /tests/v1/offloading @ApostaC
-/tests/v1/determinism @yewentao256 

 # Transformers modeling backend
 /vllm/model_executor/models/transformers @hmellor
@@ -117,15 +111,15 @@ mkdocs.yaml @hmellor
 /vllm/transformers_utils/tokenizers/mistral.py @patrickvonplaten

 # Kernels
-/vllm/v1/attention/ops/chunked_prefill_paged_decode.py @tdoublep
-/vllm/v1/attention/ops/triton_unified_attention.py @tdoublep
+/vllm/attention/ops/chunked_prefill_paged_decode.py @tdoublep
+/vllm/attention/ops/triton_unified_attention.py @tdoublep

 # ROCm related: specify owner with write access to notify AMD folks for careful code review
 /vllm/**/*rocm* @tjtanaa
 /docker/Dockerfile.rocm* @gshtras @tjtanaa
 /vllm/v1/attention/backends/rocm*.py @gshtras @tjtanaa
 /vllm/v1/attention/backends/mla/rocm*.py @gshtras @tjtanaa
-/vllm/v1/attention/ops/rocm*.py @gshtras @tjtanaa
+/vllm/attention/ops/rocm*.py @gshtras @tjtanaa
 /vllm/model_executor/layers/fused_moe/rocm*.py @gshtras @tjtanaa
 /csrc/rocm @gshtras @tjtanaa
 /requirements/*rocm* @tjtanaa
@@ -147,13 +141,12 @@ mkdocs.yaml @hmellor
 /requirements/kv_connectors.txt @NickLucche

 # Pooling models
-/examples/pooling @noooop
+/examples/*/pooling/ @noooop
 /tests/models/*/pooling* @noooop
 /tests/entrypoints/pooling @noooop
-/vllm/entrypoints/pooling @noooop
 /vllm/config/pooler.py @noooop
 /vllm/pooling_params.py @noooop
-/vllm/model_executor/layers/pooler @noooop
+/vllm/model_executor/layers/pooler.py @noooop

 # Security guide and policies
 /docs/usage/security.md @russellb
--- a/.github/mergify.yml
+++ b/.github/mergify.yml
@@ -14,52 +14,6 @@ pull_request_rules:
    comment:
      message: "Documentation preview: https://vllm--{{number}}.org.readthedocs.build/en/{{number}}/"

- name: comment-pre-commit-failure
-  description: Comment on PR when pre-commit check fails
-  conditions:
-    - status-failure=pre-commit
-    - -closed
-    - -draft
-  actions:
-    comment:
-      message: |
-        Hi @{{author}}, the pre-commit checks have failed. Please run:
-
-        ```bash 
-        uv pip install pre-commit
-        pre-commit install
-        pre-commit run --all-files
-        ```
-
-        Then, commit the changes and push to your branch.
-
-        For future commits, `pre-commit` will run automatically on changed files before each commit.
-
-        > [!TIP]
-        > <details>
-        > <summary>Is <code>mypy</code> or <code>markdownlint</code> failing?</summary>
-        > <br/>
-        > <code>mypy</code> and <code>markdownlint</code> are run differently in CI. If the failure is related to either of these checks, please use the following commands to run them locally:
-        >
-        > ```bash
-        > # For mypy (substitute "3.10" with the failing version if needed)
-        > pre-commit run --hook-stage manual mypy-3.10
-        > # For markdownlint
-        > pre-commit run --hook-stage manual markdownlint
-        > ```
-        > </details>
-
- name: comment-dco-failure
-  description: Comment on PR when DCO check fails
-  conditions:
-    - status-failure=dco
-    - -closed
-    - -draft
-  actions:
-    comment:
-      message: |
-        Hi @{{author}}, the DCO check has failed. Please click on DCO in the Checks section for instructions on how to resolve this.
-
 - name: label-ci-build
  description: Automatically apply ci/build label
  conditions:
@@ -186,7 +140,7 @@ pull_request_rules:
      - files~=^tests/entrypoints/test_context.py
      - files~=^vllm/model_executor/models/.*gpt[-_]?oss.*\.py
      - files~=^vllm/model_executor/layers/.*gpt[-_]?oss.*\.py
-      - files~=^vllm/entrypoints/openai/parser/harmony_utils.py
+      - files~=^vllm/entrypoints/harmony_utils.py
      - files~=^vllm/entrypoints/tool_server.py
      - files~=^vllm/entrypoints/tool.py
      - files~=^vllm/entrypoints/context.py
@@ -222,10 +176,10 @@ pull_request_rules:
      - files~=^csrc/rocm/
      - files~=^docker/Dockerfile.rocm
      - files~=^requirements/rocm.*\.txt
+      - files~=^vllm/attention/backends/rocm.*\.py
+      - files~=^vllm/attention/ops/rocm.*\.py
      - files~=^vllm/model_executor/layers/fused_moe/rocm.*\.py
-      - files~=^vllm/v1/attention/backends/rocm.*\.py
      - files~=^vllm/v1/attention/backends/mla/rocm.*\.py
-      - files~=^vllm/v1/attention/ops/rocm.*\.py
      - files~=^tests/kernels/.*_rocm.*\.py
      - files=vllm/platforms/rocm.py
      - title~=(?i)AMD
@@ -235,20 +189,6 @@ pull_request_rules:
      add:
        - rocm

- name: label-cpu
-  description: Automatically apply cpu label
-  conditions:
-    - label != stale
-    - files~=^(?!.*kv_offload)(?!.*cpu_offload).*\bcpu.*
-  actions:
-    label:
-      add:
-        - cpu
-    assign:
-      users:
-        - "fadara01"
-        - "aditew01"
-
 - name: label-structured-output
  description: Automatically apply structured-output label
  conditions:
@@ -349,18 +289,6 @@ pull_request_rules:
      add:
        - tool-calling

- name: auto-rebase if approved, ready, and 40 commits behind main
-  conditions:
-    - base = main
-    - label=ready
-    - "#approved-reviews-by >= 1"
-    - "#commits-behind >= 40"
-    - -closed
-    - -draft
-    - -conflict
-  actions:
-    rebase: {}
-
 - name: ping author on conflicts and add 'needs-rebase' label
  conditions:
    - label != stale
@@ -430,4 +358,4 @@ pull_request_rules:
  actions:
    label:
      add:
-        - kv-connector
+        - kv-connector
--- a/.github/workflows/cleanup_pr_body.yml
+++ b/.github/workflows/cleanup_pr_body.yml
@@ -13,10 +13,10 @@ jobs:

    steps:
      - name: Checkout repository
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2

      - name: Set up Python
-        uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # v6.1.0
+        uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
        with:
          python-version: '3.12'

--- a/.github/workflows/issue_autolabel.yml
+++ b/.github/workflows/issue_autolabel.yml
@@ -105,31 +105,6 @@ jobs:
                  }
                ],
              },
-              cpu: {
-                // Keyword search - matches whole words only (with word boundaries)
-                keywords: [
-                  {
-                    term: "CPU Backend",
-                    searchIn: "title"
-                  },
-                  {
-                    term: "x86",
-                    searchIn: "title"
-                  },
-                  {
-                    term: "ARM",
-                    searchIn: "title"
-                  },
-                  {
-                    term: "Apple Silicon",
-                    searchIn: "title"
-                  },
-                  {
-                    term: "IBM Z",
-                    searchIn: "title"
-                  },
-                ],
-              },
              // Add more label configurations here as needed
              // example: {
              //   keywords: [...],
--- a/.github/workflows/macos-smoke-test.yml
+++ b/.github/workflows/macos-smoke-test.yml
@@ -9,10 +9,10 @@ on:
 jobs:
  macos-m1-smoke-test:
    runs-on: macos-latest
-    timeout-minutes: 30
+    timeout-minutes: 20

    steps:
-      - uses: actions/checkout@v6.0.1
+      - uses: actions/checkout@v4

      - uses: astral-sh/setup-uv@v7
        with:
@@ -37,14 +37,15 @@ jobs:
      - name: Verify installation
        run: |
          python -c "import vllm; print(f'vLLM version: {vllm.__version__}')"
+          python -c "import torch; print(f'PyTorch: {torch.__version__}')"

      - name: Smoke test vllm serve
+        timeout-minutes: 10
        run: |
          # Start server in background
          vllm serve Qwen/Qwen3-0.6B \
-            --max-model-len=2K \
+            --max-model-len=2048 \
            --load-format=dummy \
-            --hf-overrides '{"num_hidden_layers": 2}' \
            --enforce-eager \
            --port 8000 &

--- a/.github/workflows/pre-commit.yml
+++ b/.github/workflows/pre-commit.yml
@@ -16,8 +16,8 @@ jobs:
  pre-commit:
    runs-on: ubuntu-latest
    steps:
-    - uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1
-    - uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # v6.1.0
+    - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+    - uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
      with:
        python-version: "3.12"
    - run: echo "::add-matcher::.github/workflows/matchers/actionlint.json"
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -7,15 +7,13 @@ on:

 jobs:
  close-issues-and-pull-requests:
-    # Prevents triggering on forks or other repos
-    if: github.repository == 'vllm-project/vllm'
    permissions:
      issues: write
      pull-requests: write
      actions: write
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # v10.1.1
+      - uses: actions/stale@5f858e3efba33a5ca4407a664cc011ad407f2008 # v10.1.0
        with:
          # Increasing this value ensures that changes to this workflow
          # propagate to all issues and PRs in days rather than months
--- a/.gitignore
+++ b/.gitignore
@@ -4,9 +4,6 @@
 # vllm-flash-attn built from source
 vllm/vllm_flash_attn/*

-# OpenAI triton kernels copied from source
-vllm/third_party/triton_kernels/*
-
 # triton jit
 .triton

@@ -227,8 +224,3 @@ ep_kernels_workspace/

 # Allow tracked library source folders under submodules (e.g., benchmarks/lib)
 !vllm/benchmarks/lib/
-
-# Generated gRPC protobuf files (compiled at build time from vllm_engine.proto)
-vllm/grpc/vllm_engine_pb2.py
-vllm/grpc/vllm_engine_pb2_grpc.py
-vllm/grpc/vllm_engine_pb2.pyi
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -56,8 +56,8 @@ endif()
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from docker/Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.9.1")
-set(TORCH_SUPPORTED_VERSION_ROCM "2.9.1")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.9.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.9.0")

 #
 # Try to find python package with an executable that exactly matches
@@ -136,7 +136,7 @@ elseif(HIP_FOUND)

  # ROCm 5.X and 6.X
  if (ROCM_VERSION_DEV_MAJOR GREATER_EQUAL 5 AND
-      Torch_VERSION VERSION_LESS ${TORCH_SUPPORTED_VERSION_ROCM})
+      NOT Torch_VERSION VERSION_EQUAL ${TORCH_SUPPORTED_VERSION_ROCM})
    message(WARNING "Pytorch version >= ${TORCH_SUPPORTED_VERSION_ROCM} "
      "expected for ROCm build, saw ${Torch_VERSION} instead.")
  endif()
@@ -282,7 +282,6 @@ endif()
 set(VLLM_EXT_SRC
  "csrc/mamba/mamba_ssm/selective_scan_fwd.cu"
  "csrc/cache_kernels.cu"
-  "csrc/cache_kernels_fused.cu"
  "csrc/attention/paged_attention_v1.cu"
  "csrc/attention/paged_attention_v2.cu"
  "csrc/attention/merge_attn_states.cu"
@@ -308,7 +307,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
  SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")

  # Set CUTLASS_REVISION. Used for FetchContent. Also fixes some bogus messages when building.
-  set(CUTLASS_REVISION "v4.2.1")
+  set(CUTLASS_REVISION "v4.2.1" CACHE STRING "CUTLASS revision to use")

  # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
  if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
@@ -355,22 +354,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
  # Only build Marlin kernels if we are building for at least some compatible archs.
  # Keep building Marlin for 9.0 as there are some group sizes and shapes that
  # are not supported by Machete yet.
-
-  # marlin arches for fp16 output
-  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0+PTX" "${CUDA_ARCHS}")
-  # marlin has limited support for turing
-  cuda_archs_loose_intersection(MARLIN_SM75_ARCHS "7.5" "${CUDA_ARCHS}")
-  # marlin arches for bf16 output (we need 9.0 for bf16 atomicAdd PTX)
-  cuda_archs_loose_intersection(MARLIN_BF16_ARCHS "8.0+PTX;9.0+PTX" "${CUDA_ARCHS}")
-  # marlin arches for fp8 input
-  # - sm80 doesn't support fp8 computation
-  # - sm90 and sm100 don't support QMMA.16832.F32.E4M3.E4M3 SAAS instruction
-  # so we only enable fp8 computation for SM89 (e.g. RTX 40x0)  and 12.0 (e.g. RTX 50x0)
-  cuda_archs_loose_intersection(MARLIN_FP8_ARCHS "8.9;12.0" "${CUDA_ARCHS}")
-  # marlin arches for other files
-  cuda_archs_loose_intersection(MARLIN_OTHER_ARCHS "7.5;8.0+PTX" "${CUDA_ARCHS}")
-
-  if (MARLIN_OTHER_ARCHS)
+  # 9.0 for latest bf16 atomicAdd PTX
+  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0+PTX;9.0+PTX" "${CUDA_ARCHS}")
+  if (MARLIN_ARCHS)

    #
    # For the Marlin kernels we automatically generate sources for various
@@ -379,18 +365,16 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
    set(MARLIN_GEN_SCRIPT
      ${CMAKE_CURRENT_SOURCE_DIR}/csrc/quantization/gptq_marlin/generate_kernels.py)
    file(MD5 ${MARLIN_GEN_SCRIPT} MARLIN_GEN_SCRIPT_HASH)
-    list(JOIN CUDA_ARCHS "," CUDA_ARCHS_STR)
-    set(MARLIN_GEN_SCRIPT_HASH_AND_ARCH "${MARLIN_GEN_SCRIPT_HASH}(ARCH:${CUDA_ARCHS_STR})")

-    message(STATUS "Marlin generation script hash: ${MARLIN_GEN_SCRIPT_HASH_AND_ARCH}")
-    message(STATUS "Last run Marlin generate script hash: $CACHE{MARLIN_GEN_SCRIPT_HASH_AND_ARCH}")
+    message(STATUS "Marlin generation script hash: ${MARLIN_GEN_SCRIPT_HASH}")
+    message(STATUS "Last run Marlin generate script hash: $CACHE{MARLIN_GEN_SCRIPT_HASH}")

-    if (NOT DEFINED CACHE{MARLIN_GEN_SCRIPT_HASH_AND_ARCH}
-        OR NOT $CACHE{MARLIN_GEN_SCRIPT_HASH_AND_ARCH} STREQUAL ${MARLIN_GEN_SCRIPT_HASH_AND_ARCH})
+    if (NOT DEFINED CACHE{MARLIN_GEN_SCRIPT_HASH}
+        OR NOT $CACHE{MARLIN_GEN_SCRIPT_HASH} STREQUAL ${MARLIN_GEN_SCRIPT_HASH})
      execute_process(
        COMMAND ${CMAKE_COMMAND} -E env
-        PYTHONPATH=$ENV{PYTHONPATH}
-          ${Python_EXECUTABLE} ${MARLIN_GEN_SCRIPT} ${CUDA_ARCHS_STR}
+        PYTHONPATH=$PYTHONPATH
+          ${Python_EXECUTABLE} ${MARLIN_GEN_SCRIPT}
        RESULT_VARIABLE marlin_generation_result
        OUTPUT_VARIABLE marlin_generation_result
        OUTPUT_FILE ${CMAKE_CURRENT_BINARY_DIR}/marlin_generation.log
@@ -403,76 +387,40 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
                            "\nCheck the log for details: "
                            "${CMAKE_CURRENT_BINARY_DIR}/marlin_generation.log")
      else()
-        set(MARLIN_GEN_SCRIPT_HASH_AND_ARCH ${MARLIN_GEN_SCRIPT_HASH_AND_ARCH}
-            CACHE STRING "Last run Marlin generate script hash and arch" FORCE)
+        set(MARLIN_GEN_SCRIPT_HASH ${MARLIN_GEN_SCRIPT_HASH}
+            CACHE STRING "Last run Marlin generate script hash" FORCE)
        message(STATUS "Marlin generation completed successfully.")
      endif()
    else()
      message(STATUS "Marlin generation script has not changed, skipping generation.")
    endif()

-    if (MARLIN_ARCHS)
-      file(GLOB MARLIN_TEMPLATE_KERNEL_SRC "csrc/quantization/gptq_marlin/sm80_kernel_*_float16.cu")
-      set_gencode_flags_for_srcs(
-        SRCS "${MARLIN_TEMPLATE_KERNEL_SRC}"
-        CUDA_ARCHS "${MARLIN_ARCHS}")
-      if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
-        set_source_files_properties(${MARLIN_TEMPLATE_KERNEL_SRC}
-          PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
-      endif()
-      list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_KERNEL_SRC})
-
-      file(GLOB MARLIN_TEMPLATE_BF16_KERNEL_SRC "csrc/quantization/gptq_marlin/sm80_kernel_*_bfloat16.cu")
-      set_gencode_flags_for_srcs(
-        SRCS "${MARLIN_TEMPLATE_BF16_KERNEL_SRC}"
-        CUDA_ARCHS "${MARLIN_BF16_ARCHS}")
-      if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
-        set_source_files_properties(${MARLIN_TEMPLATE_BF16_KERNEL_SRC}
-          PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
-      endif()
-      list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_BF16_KERNEL_SRC})
+    file(GLOB MARLIN_TEMPLATE_KERNEL_SRC "csrc/quantization/gptq_marlin/kernel_*.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${MARLIN_TEMPLATE_KERNEL_SRC}"
+      CUDA_ARCHS "${MARLIN_ARCHS}")
+    if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
+      set_source_files_properties(${MARLIN_TEMPLATE_KERNEL_SRC}
+        PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
    endif()

-    if (MARLIN_SM75_ARCHS) 
-      file(GLOB MARLIN_TEMPLATE_SM75_KERNEL_SRC "csrc/quantization/gptq_marlin/sm75_kernel_*.cu")
-      set_gencode_flags_for_srcs(
-        SRCS "${MARLIN_TEMPLATE_SM75_KERNEL_SRC}"
-        CUDA_ARCHS "${MARLIN_SM75_ARCHS}")
-      if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
-        set_source_files_properties(${MARLIN_TEMPLATE_SM75_KERNEL_SRC}
-          PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
-      endif()
-      list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_SM75_KERNEL_SRC})
-    endif()
-
-    if (MARLIN_FP8_ARCHS) 
-      file(GLOB MARLIN_TEMPLATE_FP8_KERNEL_SRC "csrc/quantization/gptq_marlin/sm89_kernel_*.cu")
-      set_gencode_flags_for_srcs(
-        SRCS "${MARLIN_TEMPLATE_FP8_KERNEL_SRC}"
-        CUDA_ARCHS "${MARLIN_FP8_ARCHS}")
-      if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
-        set_source_files_properties(${MARLIN_TEMPLATE_FP8_KERNEL_SRC}
-          PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
-      endif()
-      list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_FP8_KERNEL_SRC})
-    endif()
+    list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_KERNEL_SRC})

    set(MARLIN_SRCS
       "csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
       "csrc/quantization/gptq_marlin/gptq_marlin.cu"
-       "csrc/quantization/gptq_marlin/marlin_int4_fp8_preprocess.cu"
       "csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
       "csrc/quantization/gptq_marlin/awq_marlin_repack.cu")
    set_gencode_flags_for_srcs(
      SRCS "${MARLIN_SRCS}"
-      CUDA_ARCHS "${MARLIN_OTHER_ARCHS}")
+      CUDA_ARCHS "${MARLIN_ARCHS}")
    if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
-      set_source_files_properties(${MARLIN_SRCS}
+      set_source_files_properties("csrc/quantization/gptq_marlin/gptq_marlin.cu"
        PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
    endif()
    list(APPEND VLLM_EXT_SRC "${MARLIN_SRCS}")

-    message(STATUS "Building Marlin kernels for archs: ${MARLIN_OTHER_ARCHS}")
+    message(STATUS "Building Marlin kernels for archs: ${MARLIN_ARCHS}")
  else()
    message(STATUS "Not building Marlin kernels as no compatible archs found"
                   " in CUDA target architectures")
@@ -656,15 +604,12 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
    set(SRCS
      "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
      "csrc/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu"
-      "csrc/quantization/fp4/nvfp4_experts_quant.cu"
-      "csrc/quantization/fp4/nvfp4_scaled_mm_sm120_kernels.cu"
-      "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu")
+      "csrc/quantization/fp4/nvfp4_scaled_mm_sm120_kernels.cu")
    set_gencode_flags_for_srcs(
      SRCS "${SRCS}"
      CUDA_ARCHS "${FP4_ARCHS}")
    list(APPEND VLLM_EXT_SRC "${SRCS}")
    list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM120=1")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM120=1")
    message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
  else()
    message(STATUS "Not building NVFP4 as no compatible archs were found.")
@@ -800,6 +745,24 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
  else()
    cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
  endif()
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
+    set(SRCS "csrc/quantization/w8a8/cutlass/moe/blockwise_scaled_group_mm_sm100.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${SRCS}"
+      CUDA_ARCHS "${SCALED_MM_ARCHS}")
+    list(APPEND VLLM_EXT_SRC "${SRCS}")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
+    message(STATUS "Building blockwise_scaled_group_mm_sm100 for archs: ${SCALED_MM_ARCHS}")
+  else()
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
+      message(STATUS "Not building blockwise_scaled_group_mm_sm100 kernels as CUDA Compiler version is "
+                     "not >= 12.8, we recommend upgrading to CUDA 12.8 or later "
+                     "if you intend on running FP8 quantized MoE models on Blackwell.")
+    else()
+      message(STATUS "Not building blockwise_scaled_group_mm_sm100 as no compatible archs found "
+                     "in CUDA target architectures")
+    endif()
+  endif()

  #
  # Machete kernels
@@ -823,7 +786,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
        OR NOT $CACHE{MACHETE_GEN_SCRIPT_HASH} STREQUAL ${MACHETE_GEN_SCRIPT_HASH})
      execute_process(
        COMMAND ${CMAKE_COMMAND} -E env
-        PYTHONPATH=${CMAKE_CURRENT_SOURCE_DIR}/csrc/cutlass_extensions/:${CUTLASS_DIR}/python/:${VLLM_PYTHON_PATH}:$ENV{PYTHONPATH}
+        PYTHONPATH=${CMAKE_CURRENT_SOURCE_DIR}/csrc/cutlass_extensions/:${CUTLASS_DIR}/python/:${VLLM_PYTHON_PATH}:$PYTHONPATH
          ${Python_EXECUTABLE} ${MACHETE_GEN_SCRIPT}
        RESULT_VARIABLE machete_generation_result
        OUTPUT_VARIABLE machete_generation_output
@@ -875,10 +838,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
  cuda_archs_loose_intersection(W4A8_ARCHS "9.0a" "${CUDA_ARCHS}")
  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND W4A8_ARCHS)
    set(SRCS
-       "csrc/quantization/cutlass_w4a8/w4a8_mm_entry.cu"
-       "csrc/quantization/cutlass_w4a8/w4a8_grouped_mm_entry.cu"
-       "csrc/quantization/cutlass_w4a8/w4a8_utils.cu"
-       )
+       "csrc/quantization/cutlass_w4a8/w4a8_mm_entry.cu")

    set_gencode_flags_for_srcs(
      SRCS "${SRCS}"
@@ -948,6 +908,7 @@ target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
 set(VLLM_MOE_EXT_SRC
  "csrc/moe/torch_bindings.cpp"
  "csrc/moe/moe_align_sum_kernels.cu"
+  "csrc/moe/moe_lora_align_sum_kernels.cu"
  "csrc/moe/topk_softmax_kernels.cu")

 if(VLLM_GPU_LANG STREQUAL "CUDA")
@@ -977,20 +938,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
    CUDA_ARCHS "${CUDA_ARCHS}")

  list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
-  # moe marlin arches
-  # note that we always set `use_atomic_add=False` for moe marlin now,
-  # so we don't need 9.0 for bf16 atomicAdd PTX
-  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0+PTX" "${CUDA_ARCHS}")
-  # moe marlin has limited support for turing
-  cuda_archs_loose_intersection(MARLIN_MOE_SM75_ARCHS "7.5" "${CUDA_ARCHS}")
-  # moe marlin arches for fp8 input
-  # - sm80 doesn't support fp8 computation
-  # - sm90 and sm100 don't support QMMA.16832.F32.E4M3.E4M3 SAAS instruction
-  # so we only enable fp8 computation for SM89 (e.g. RTX 40x0)  and 12.0 (e.g. RTX 50x0)
-  cuda_archs_loose_intersection(MARLIN_MOE_FP8_ARCHS "8.9;12.0" "${CUDA_ARCHS}")
-  # moe marlin arches for other files
-  cuda_archs_loose_intersection(MARLIN_MOE_OTHER_ARCHS "7.5;8.0+PTX" "${CUDA_ARCHS}")
-  if (MARLIN_MOE_OTHER_ARCHS)
+  # 9.0 for latest bf16 atomicAdd PTX
+  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0+PTX;9.0+PTX" "${CUDA_ARCHS}")
+  if (MARLIN_MOE_ARCHS)

    #
    # For the Marlin MOE kernels we automatically generate sources for various
@@ -999,18 +949,16 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
    set(MOE_MARLIN_GEN_SCRIPT
      ${CMAKE_CURRENT_SOURCE_DIR}/csrc/moe/marlin_moe_wna16/generate_kernels.py)
    file(MD5 ${MOE_MARLIN_GEN_SCRIPT} MOE_MARLIN_GEN_SCRIPT_HASH)
-    list(JOIN CUDA_ARCHS "," CUDA_ARCHS_STR)
-    set(MOE_MARLIN_GEN_SCRIPT_HASH_AND_ARCH "${MOE_MARLIN_GEN_SCRIPT_HASH}(ARCH:${CUDA_ARCHS_STR})")

-    message(STATUS "Marlin MOE generation script hash with arch: ${MOE_MARLIN_GEN_SCRIPT_HASH_AND_ARCH}")
-    message(STATUS "Last run Marlin MOE generate script hash with arch: $CACHE{MOE_MARLIN_GEN_SCRIPT_HASH_AND_ARCH}")
+    message(STATUS "Marlin MOE generation script hash: ${MOE_MARLIN_GEN_SCRIPT_HASH}")
+    message(STATUS "Last run Marlin MOE generate script hash: $CACHE{MOE_MARLIN_GEN_SCRIPT_HASH}")

-    if (NOT DEFINED CACHE{MOE_MARLIN_GEN_SCRIPT_HASH_AND_ARCH}
-        OR NOT $CACHE{MOE_MARLIN_GEN_SCRIPT_HASH_AND_ARCH} STREQUAL ${MOE_MARLIN_GEN_SCRIPT_HASH_AND_ARCH})
+    if (NOT DEFINED CACHE{MOE_MARLIN_GEN_SCRIPT_HASH}
+        OR NOT $CACHE{MOE_MARLIN_GEN_SCRIPT_HASH} STREQUAL ${MOE_MARLIN_GEN_SCRIPT_HASH})
      execute_process(
        COMMAND ${CMAKE_COMMAND} -E env
-        PYTHONPATH=$ENV{PYTHONPATH}
-          ${Python_EXECUTABLE} ${MOE_MARLIN_GEN_SCRIPT} ${CUDA_ARCHS_STR}
+        PYTHONPATH=$PYTHONPATH
+          ${Python_EXECUTABLE} ${MOE_MARLIN_GEN_SCRIPT}
        RESULT_VARIABLE moe_marlin_generation_result
        OUTPUT_VARIABLE moe_marlin_generation_output
        OUTPUT_FILE ${CMAKE_CURRENT_BINARY_DIR}/moe_marlin_generation.log
@@ -1023,7 +971,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
                            "\nCheck the log for details: "
                            "${CMAKE_CURRENT_BINARY_DIR}/moe_marlin_generation.log")
      else()
-        set(MOE_MARLIN_GEN_SCRIPT_HASH_AND_ARCH ${MOE_MARLIN_GEN_SCRIPT_HASH_AND_ARCH}
+        set(MOE_MARLIN_GEN_SCRIPT_HASH ${MOE_MARLIN_GEN_SCRIPT_HASH}
            CACHE STRING "Last run Marlin MOE generate script hash" FORCE)
        message(STATUS "Marlin MOE generation completed successfully.")
      endif()
@@ -1031,53 +979,18 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
      message(STATUS "Marlin MOE generation script has not changed, skipping generation.")
    endif()

-    if (MARLIN_MOE_ARCHS)
-      file(GLOB MARLIN_MOE_SRC "csrc/moe/marlin_moe_wna16/sm80_kernel_*.cu")
-      set_gencode_flags_for_srcs(
-        SRCS "${MARLIN_MOE_SRC}"
-        CUDA_ARCHS "${MARLIN_MOE_ARCHS}")
-      if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
-        set_source_files_properties(${MARLIN_MOE_SRC}
-          PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
-      endif()
-      list(APPEND VLLM_MOE_EXT_SRC ${MARLIN_MOE_SRC})
-    endif()
-
-    if (MARLIN_MOE_SM75_ARCHS) 
-      file(GLOB MARLIN_MOE_SM75_SRC "csrc/moe/marlin_moe_wna16/sm75_kernel_*.cu")
-      set_gencode_flags_for_srcs(
-        SRCS "${MARLIN_MOE_SM75_SRC}"
-        CUDA_ARCHS "${MARLIN_MOE_SM75_ARCHS}")
-      if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
-        set_source_files_properties(${MARLIN_MOE_SM75_SRC}
-          PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
-      endif()
-      list(APPEND VLLM_MOE_EXT_SRC ${MARLIN_MOE_SM75_SRC})
-    endif()
-
-    if (MARLIN_MOE_FP8_ARCHS)
-      file(GLOB MARLIN_MOE_FP8_SRC "csrc/moe/marlin_moe_wna16/sm89_kernel_*.cu")
-      set_gencode_flags_for_srcs(
-        SRCS "${MARLIN_MOE_FP8_SRC}"
-        CUDA_ARCHS "${MARLIN_MOE_FP8_ARCHS}")
-      if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
-        set_source_files_properties(${MARLIN_MOE_FP8_SRC}
-          PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
-      endif()
-      list(APPEND VLLM_MOE_EXT_SRC ${MARLIN_MOE_FP8_SRC})
-    endif()
-
-    set(MARLIN_MOE_OTHER_SRC "csrc/moe/marlin_moe_wna16/ops.cu")
+    file(GLOB MOE_WNAA16_MARLIN_SRC "csrc/moe/marlin_moe_wna16/*.cu")
    set_gencode_flags_for_srcs(
-      SRCS "${MARLIN_MOE_OTHER_SRC}"
-      CUDA_ARCHS "${MARLIN_MOE_OTHER_ARCHS}")
+      SRCS "${MOE_WNAA16_MARLIN_SRC}"
+      CUDA_ARCHS "${MARLIN_MOE_ARCHS}")
    if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
-      set_source_files_properties(${MARLIN_MOE_OTHER_SRC}
+      set_source_files_properties(${MOE_WNAA16_MARLIN_SRC}
        PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
    endif()
-    list(APPEND VLLM_MOE_EXT_SRC "${MARLIN_MOE_OTHER_SRC}")

-    message(STATUS "Building Marlin MOE kernels for archs: ${MARLIN_MOE_OTHER_ARCHS}")
+    list(APPEND VLLM_MOE_EXT_SRC ${MOE_WNAA16_MARLIN_SRC})
+
+    message(STATUS "Building Marlin MOE kernels for archs: ${MARLIN_MOE_ARCHS}")
  else()
    message(STATUS "Not building Marlin MOE kernels as no compatible archs found"
                   " in CUDA target architectures")
@@ -1117,11 +1030,6 @@ if(VLLM_GPU_LANG STREQUAL "HIP")
    WITH_SOABI)
 endif()

-# For CUDA and HIP builds also build the triton_kernels external package.
-if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
-    include(cmake/external_projects/triton_kernels.cmake)
-endif()
-
 # For CUDA we also build and ship some external projects.
 if (VLLM_GPU_LANG STREQUAL "CUDA")
    include(cmake/external_projects/flashmla.cmake)
--- a/README.md
+++ b/README.md
@@ -14,8 +14,50 @@ Easy, fast, and cheap LLM serving for everyone
 | <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://blog.vllm.ai/"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
 </p>

-🔥 We have built a vllm website to help you get started with vllm. Please visit [vllm.ai](https://vllm.ai) to learn more.
-For events, please visit [vllm.ai/events](https://vllm.ai/events) to join us.
+---
+Join us at the [PyTorch Conference, October 22-23](https://events.linuxfoundation.org/pytorch-conference/) and [Ray Summit, November 3-5](https://www.anyscale.com/ray-summit/2025) in San Francisco for our latest updates on vLLM and to meet the vLLM team! Register now for the largest vLLM community events of the year!
+
+---
+
+*Latest News* 🔥
+
+- [2025/11] We hosted [the first vLLM Europe Meetup in Zurich](https://luma.com/0gls27kb) focused on quantization, distributed inference, and reinforcement learning at scale with speakers from Mistral, IBM, and Red Hat. Please find the meetup slides [here](https://docs.google.com/presentation/d/1UC9PTLCHYXQpOmJDSFg6Sljra3iVXzc09DeEI7dnxMc/edit?usp=sharing) and recording [here](https://www.youtube.com/watch?v=6m6ZE6yVEDI)
+- [2025/11] We hosted [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/xSrYXjNgr1HbCP4ExYNG1w) focusing on distributed inference and diverse accelerator support with vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1nQJ8ZkLSjKxvu36sSHaceVXtttbLvvu-?usp=drive_link).
+- [2025/10] We hosted [vLLM Shanghai Meetup](https://mp.weixin.qq.com/s/__xb4OyOsImz-9eAVrdlcg) focused on hands-on vLLM inference optimization! Please find the meetup slides [here](https://drive.google.com/drive/folders/1KqwjsFJLfEsC8wlDugnrR61zsWHt94Q6).
+- [2025/09] We hosted [vLLM Toronto Meetup](https://luma.com/e80e0ymm) focused on tackling inference at scale and speculative decoding with speakers from NVIDIA and Red Hat! Please find the meetup slides [here](https://docs.google.com/presentation/d/1IYJYmJcu9fLpID5N5RbW_vO0XLo0CGOR14IXOjB61V8/edit?usp=sharing).
+- [2025/08] We hosted [vLLM Shenzhen Meetup](https://mp.weixin.qq.com/s/k8ZBO1u2_2odgiKWH_GVTQ) focusing on the ecosystem around vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Ua2SVKVSu-wp5vou_6ElraDt2bnKhiEA).
+- [2025/08] We hosted [vLLM Singapore Meetup](https://www.sginnovate.com/event/vllm-sg-meet). We shared V1 updates, disaggregated serving and MLLM speedups with speakers from Embedded LLM, AMD, WekaIO, and A*STAR. Please find the meetup slides [here](https://drive.google.com/drive/folders/1ncf3GyqLdqFaB6IeB834E5TZJPLAOiXZ?usp=sharing).
+- [2025/08] We hosted [vLLM Shanghai Meetup](https://mp.weixin.qq.com/s/pDmAXHcN7Iqc8sUKgJgGtg) focusing on building, developing, and integrating with vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1OvLx39wnCGy_WKq8SiVKf7YcxxYI3WCH).
+- [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement [here](https://pytorch.org/blog/pytorch-foundation-welcomes-vllm/).
+- [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
+
+<details>
+<summary>Previous News</summary>
+
+- [2025/08] We hosted [vLLM Korea Meetup](https://luma.com/cgcgprmh) with Red Hat and Rebellions! We shared the latest advancements in vLLM along with project spotlights from the vLLM Korea community. Please find the meetup slides [here](https://drive.google.com/file/d/1bcrrAE1rxUgx0mjIeOWT6hNe2RefC5Hm/view).
+- [2025/08] We hosted [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/dgkWg1WFpWGO2jCdTqQHxA) focusing on large-scale LLM deployment! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Pid6NSFLU43DZRi0EaTcPgXsAzDvbBqF) and the recording [here](https://www.chaspark.com/#/live/1166916873711665152).
+- [2025/05] We hosted [NYC vLLM Meetup](https://lu.ma/c1rqyf1f)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1_q_aW_ioMJWUImf1s1YM-ZhjXz8cUeL0IJvaquOYBeA/edit?usp=sharing).
+- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
+- [2025/03] We hosted [vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).
+- [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
+- [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
+- [2025/02] We hosted [the ninth vLLM meetup](https://lu.ma/h7g3kuj9) with Meta! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1jzC_PZVXrVNSFVCW-V4cFXb6pn7zZ2CyP_Flwo05aqg/edit?usp=sharing) and AMD [here](https://drive.google.com/file/d/1Zk5qEJIkTmlQ2eQcXQZlljAx3m9s7nwn/view?usp=sharing). The slides from Meta will not be posted.
+- [2025/01] We hosted [the eighth vLLM meetup](https://lu.ma/zep56hui) with Google Cloud! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing), and Google Cloud team [here](https://drive.google.com/file/d/1h24pHewANyRL11xy5dXUbvRC9F9Kkjix/view?usp=sharing).
+- [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone!
+- [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing), and Snowflake team [here](https://docs.google.com/presentation/d/1qF3RkDAbOULwz9WK5TOltt2fE9t6uIc_hVNLFAaQX6A/edit?usp=sharing).
+- [2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there!
+- [2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team [here](https://docs.google.com/presentation/d/1B_KQxpHBTRa_mDF-tR6i8rWdOU5QoTZNcEg2MKZxEHM/edit?usp=sharing). Learn more from the [talks](https://www.youtube.com/playlist?list=PLzTswPQNepXl6AQwifuwUImLPFRVpksjR) from other vLLM contributors and users!
+- [2024/09] We hosted [the sixth vLLM meetup](https://lu.ma/87q3nvnh) with NVIDIA! Please find the meetup slides [here](https://docs.google.com/presentation/d/1wrLGwytQfaOTd5wCGSPNhoaW3nq0E-9wqyP7ny93xRs/edit?usp=sharing).
+- [2024/07] We hosted [the fifth vLLM meetup](https://lu.ma/lp0gyjqr) with AWS! Please find the meetup slides [here](https://docs.google.com/presentation/d/1RgUD8aCfcHocghoP3zmXzck9vX3RCI9yfUAB2Bbcl4Y/edit?usp=sharing).
+- [2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog post [here](https://blog.vllm.ai/2024/07/23/llama31.html).
+- [2024/06] We hosted [the fourth vLLM meetup](https://lu.ma/agivllm) with Cloudflare and BentoML! Please find the meetup slides [here](https://docs.google.com/presentation/d/1iJ8o7V2bQEi0BFEljLTwc5G1S10_Rhv3beed5oB0NJ4/edit?usp=sharing).
+- [2024/04] We hosted [the third vLLM meetup](https://robloxandvllmmeetup2024.splashthat.com/) with Roblox! Please find the meetup slides [here](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing).
+- [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) with IBM! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing).
+- [2023/10] We hosted [the first vLLM meetup](https://lu.ma/first-vllm-meetup) with a16z! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing).
+- [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (a16z) for providing a generous grant to support the open-source development and research of vLLM.
+- [2023/06] We officially released vLLM! FastChat-vLLM integration has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai).
+
+</details>

 ---

@@ -75,6 +117,47 @@ Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more.
 We welcome and value any contributions and collaborations.
 Please check out [Contributing to vLLM](https://docs.vllm.ai/en/latest/contributing/index.html) for how to get involved.

+## Sponsors
+
+vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
+
+<!-- Note: Please sort them in alphabetical order. -->
+<!-- Note: Please keep these consistent with docs/community/sponsors.md -->
+Cash Donations:
+
+- a16z
+- Dropbox
+- Sequoia Capital
+- Skywork AI
+- ZhenFund
+
+Compute Resources:
+
+- Alibaba Cloud
+- AMD
+- Anyscale
+- AWS
+- Crusoe Cloud
+- Databricks
+- DeepInfra
+- Google Cloud
+- Intel
+- Lambda Lab
+- Nebius
+- Novita AI
+- NVIDIA
+- Replicate
+- Roblox
+- RunPod
+- Trainy
+- UC Berkeley
+- UC San Diego
+- Volcengine
+
+Slack Sponsor: Anyscale
+
+We also have an official fundraising venue through [OpenCollective](https://opencollective.com/vllm). We plan to use the fund to support the development, maintenance, and adoption of vLLM.
+
 ## Citation

 If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs/2309.06180):
@@ -95,7 +178,7 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 - For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai)
 - For coordinating contributions and development, please use [Slack](https://slack.vllm.ai)
 - For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature
- For collaborations and partnerships, please contact us at [collaboration@vllm.ai](mailto:collaboration@vllm.ai)
+- For collaborations and partnerships, please contact us at [vllm-questions@lists.berkeley.edu](mailto:vllm-questions@lists.berkeley.edu)
 <!-- --8<-- [end:contact-us] -->

 ## Media Kit
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -1,30 +1,47 @@
 # Releasing vLLM

-vLLM releases offer a reliable version of the code base, packaged into a binary format that can be conveniently accessed via [PyPI](https://pypi.org/project/vllm). These releases also serve as key milestones for the development team to communicate with the community about newly available features, improvements, and upcoming changes that could affect users, including potential breaking changes.
+vLLM releases offer a reliable version of the code base, packaged into a binary format that can be conveniently accessed via PyPI. These releases also serve as key milestones for the development team to communicate with the community about newly available features, improvements, and upcoming changes that could affect users, including potential breaking changes.

-## Release Cadence and Versioning
+## Release Versioning

-We aim to have a regular release every 2 weeks. Since v0.12.0, regular releases increment the minor version rather than patch version. The list of past releases can be found [here](https://vllm.ai/releases).
+vLLM uses a “right-shifted” versioning scheme where a new patch release is out every 2 weeks. And patch releases contain features and bug fixes (as opposed to semver where patch release contains only backwards-compatible bug fixes). When critical fixes need to be made, special release post1 is released.

-Our version numbers are expressed in the form `vX.Y.Z`, where `X` is the major version, `Y` is the minor version, and `Z` is the patch version. They are incremented according to the following rules:
+* _major_ major architectural milestone and when incompatible API changes are made, similar to PyTorch 2.0.
+* _minor_ major features
+* _patch_ features and backwards-compatible bug fixes
+* _post1_ or _patch-1_ backwards-compatible bug fixes, either explicit or implicit post release

-* _Major_ releases are reserved for architectural milestones involving sweeping API changes, similar to PyTorch 2.0.
-* _Minor_ releases correspond to regular releases, which include new features, bug fixes and other backwards-compatible changes.
-* _Patch_ releases correspond to special releases for new models, as well as emergency patches for critical performance, functionality and security issues.
+## Release Cadence

-This versioning scheme is similar to [SemVer](https://semver.org/) for compatibility purposes, except that backwards compatibility is only guaranteed for a limited number of minor releases (see our [deprecation policy](https://docs.vllm.ai/en/latest/contributing/deprecation_policy) for details).
+Patch release is released on bi-weekly basis. Post release 1-3 days after patch release and uses same branch as patch release.
+Following is the release cadence for year 2025. All future release dates below are tentative. Please note: Post releases are optional.

-## Release Branch
+| Release Date | Patch release versions | Post Release versions |
+| --- | --- | --- |
+| Jan 2025 | 0.7.0 | --- |
+| Feb 2025 | 0.7.1, 0.7.2, 0.7.3  | --- |
+| Mar 2025 | 0.7.4, 0.7.5 | --- |
+| Apr 2025 | 0.7.6, 0.7.7 | --- |
+| May 2025 | 0.7.8, 0.7.9 | --- |
+| Jun 2025 | 0.7.10, 0.7.11 | --- |
+| Jul 2025 | 0.7.12, 0.7.13 | --- |
+| Aug 2025 | 0.7.14, 0.7.15 | --- |
+| Sep 2025 | 0.7.16, 0.7.17 | --- |
+| Oct 2025 | 0.7.18, 0.7.19 | --- |
+| Nov 2025 | 0.7.20, 0.7.21 | --- |
+| Dec 2025 | 0.7.22, 0.7.23 | --- |
+
+## Release branch

 Each release is built from a dedicated release branch.

-* For _major_ and _minor_ releases, the release branch cut is performed 1-2 days before release is live.
-* For _patch_ releases, previously cut release branch is reused.
-* Release builds are triggered via push to RC tag like `vX.Y.Z-rc1`. This enables us to build and test multiple RCs for each release.
-* Final tag: `vX.Y.Z` does not trigger the build but used for Release notes and assets.
-* After branch cut is created, we monitor the main branch for any reverts and apply these reverts to a release branch.
+* For _major_, _minor_, _patch_ releases, the release branch cut is performed 1-2 days before release is live.
+* For post releases, previously cut release branch is reused
+* Release builds are triggered via push to RC tag like vX.Y.Z-rc1 . This enables us to build and test multiple RCs for each release.
+* Final tag : vX.Y.Z does not trigger the build but used for Release notes and assets.
+* After branch cut is created we monitor the main branch for any reverts and apply these reverts to a release branch.

-### Cherry-Pick Criteria
+## Release Cherry-Pick Criteria

 After branch cut, we approach finalizing the release branch with clear criteria on what cherry picks are allowed in. Note: a cherry pick is a process to land a PR in the release branch after branch cut. These are typically limited to ensure that the team has sufficient time to complete a thorough round of testing on a stable code base.

--- a/benchmarks/auto_tune/README.md
+++ b/benchmarks/auto_tune/README.md
@@ -83,7 +83,7 @@ MIN_CACHE_HIT_PCT=0
 MAX_LATENCY_ALLOWED_MS=100000000000 # A very large number
 ```

-### 2. Maximize Throughput with a Latency Requirement
+#### 2. Maximize Throughput with a Latency Requirement

 - **Goal**: Find the best server parameters when P99 end-to-end latency must be below 500ms.
 - **Configuration**:
@@ -96,7 +96,7 @@ MIN_CACHE_HIT_PCT=0
 MAX_LATENCY_ALLOWED_MS=500
 ```

-### 3. Maximize Throughput with Prefix Caching and Latency Requirements
+#### 3. Maximize Throughput with Prefix Caching and Latency Requirements

 - **Goal**: Find the best server parameters assuming a 60% prefix cache hit rate and a latency requirement of 500ms.
 - **Configuration**:
--- a/benchmarks/auto_tune/auto_tune.sh
+++ b/benchmarks/auto_tune/auto_tune.sh
@@ -18,11 +18,6 @@ MIN_CACHE_HIT_PCT=${MIN_CACHE_HIT_PCT:-0}
 MAX_LATENCY_ALLOWED_MS=${MAX_LATENCY_ALLOWED_MS:-100000000000}
 NUM_SEQS_LIST=${NUM_SEQS_LIST:-"128 256"}
 NUM_BATCHED_TOKENS_LIST=${NUM_BATCHED_TOKENS_LIST:-"512 1024 2048 4096"}
-HOSTNAME=$(hostname)
-if [[ -z "$HOSTNAME" ]]; then
-    echo "Error: Failed to determine hostname." >&2
-    exit 1
-fi

 LOG_FOLDER="$BASE/auto-benchmark/$TAG"
 RESULT="$LOG_FOLDER/result.txt"
@@ -87,7 +82,6 @@ start_server() {
        "$MODEL"
        "--disable-log-requests"
        "--port" "8004"
-        "--host" "$HOSTNAME"
        "--gpu-memory-utilization" "$gpu_memory_utilization"
        "--max-num-seqs" "$max_num_seqs"
        "--max-num-batched-tokens" "$max_num_batched_tokens"
@@ -102,9 +96,8 @@ start_server() {
    # This correctly passes each element as a separate argument.
    if [[ -n "$profile_dir" ]]; then
        # Start server with profiling enabled
-        local profile_config_json="{\"profiler\": \"torch\", \"torch_profiler_dir\": \"$profile_dir\"}"
-        VLLM_SERVER_DEV_MODE=1 \
-            vllm serve --profiler-config "$profile_config_json" "${common_args_array[@]}" > "$vllm_log" 2>&1 &
+        VLLM_SERVER_DEV_MODE=1 VLLM_TORCH_PROFILER_DIR=$profile_dir \
+            vllm serve "${common_args_array[@]}" > "$vllm_log" 2>&1 &
    else
        # Start server without profiling
        VLLM_SERVER_DEV_MODE=1 \
@@ -119,7 +112,7 @@ start_server() {
        # since that we should always have permission to send signal to the server process.
        kill -0 $server_pid 2> /dev/null || break

-        RESPONSE=$(curl -s -X GET "http://${HOSTNAME}:8004/health" -w "%{http_code}" -o /dev/stdout)
+        RESPONSE=$(curl -s -X GET "http://0.0.0.0:8004/health" -w "%{http_code}" -o /dev/stdout)
        STATUS_CODE=$(echo "$RESPONSE" | tail -n 1)
        if [[ "$STATUS_CODE" -eq 200 ]]; then
            server_started=1
@@ -179,7 +172,6 @@ run_benchmark() {
        --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
        --num-prompts 1000 \
        --random-prefix-len $prefix_len \
-        --host "$HOSTNAME" \
        --port 8004 &> "$bm_log"
    throughput=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
    e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
@@ -195,7 +187,7 @@ run_benchmark() {
        request_rate=$((${throughput%.*} + 1))
        while ((request_rate > 0)); do
            # clear prefix cache
-            curl -X POST http://${HOSTNAME}:8004/reset_prefix_cache
+            curl -X POST http://0.0.0.0:8004/reset_prefix_cache
            sleep 5
            bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_${request_rate}.txt"
            vllm bench serve \
@@ -211,7 +203,6 @@ run_benchmark() {
                --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
                --num-prompts 100 \
                --random-prefix-len $prefix_len \
-                --host "$HOSTNAME" \
                --port 8004 &> "$bm_log"
            throughput=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
            e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
@@ -312,7 +303,6 @@ if (( $(echo "$best_throughput > 0" | bc -l) )); then
        --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
        --num-prompts 100 \
        --random-prefix-len $prefix_len \
-        --host "$HOSTNAME" \
        --port 8004 \
        --profile &> "$bm_log"
 else
--- a/benchmarks/backend_request_func.py
+++ b/benchmarks/backend_request_func.py
@@ -620,7 +620,7 @@ def get_tokenizer(
        kwargs["use_fast"] = False
    if tokenizer_mode == "mistral":
        try:
-            from vllm.tokenizers.mistral import MistralTokenizer
+            from vllm.transformers_utils.tokenizer import MistralTokenizer
        except ImportError as e:
            raise ImportError(
                "MistralTokenizer requires vllm package.\n"
--- a/benchmarks/benchmark_batch_invariance.py
+++ b/benchmarks/benchmark_batch_invariance.py
@@ -104,6 +104,7 @@ def run_benchmark_with_batch_invariant(
    random.seed(seed)

    # Set environment variables
+    os.environ["VLLM_ATTENTION_BACKEND"] = backend
    if batch_invariant:
        os.environ["VLLM_BATCH_INVARIANT"] = "1"
    else:
@@ -139,7 +140,6 @@ def run_benchmark_with_batch_invariant(
            max_model_len=max_model_len,
            dtype="bfloat16",
            tensor_parallel_size=tp_size,
-            attention_config={"backend": backend},
            enable_prefix_caching=False,
        )
        init_time = time.perf_counter() - start_init
--- a/benchmarks/benchmark_hash.py
+++ b/benchmarks/benchmark_hash.py
@@ -1,120 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-Micro benchmark comparing built-in hash(), SHA-256, and xxHash.
-
-This focuses on a single test payload shaped like the prefix-cache hash input:
-    (32-byte bytes object, 32-int tuple)
-
-Usage:
-    python benchmarks/hash_micro_benchmark.py --iterations 20000
-"""
-
-from __future__ import annotations
-
-import argparse
-import random
-import statistics
-import time
-from collections.abc import Callable, Iterable
-
-from vllm.utils.hashing import sha256, xxhash
-
-
-def _generate_test_data(seed: int) -> tuple[bytes, tuple[int, ...]]:
-    """Generate a deterministic test payload."""
-    random.seed(seed)
-    bytes_data = bytes(random.getrandbits(8) for _ in range(32))
-    int_tuple = tuple(random.randint(1, 1_000_000) for _ in range(32))
-    return (bytes_data, int_tuple)
-
-
-def _benchmark_func(func: Callable[[tuple], object], data: tuple, iterations: int):
-    """Return (avg_seconds, std_seconds) for hashing `data` `iterations` times."""
-    times: list[float] = []
-
-    # Warm-up to avoid first-run noise.
-    for _ in range(200):
-        func(data)
-
-    for _ in range(iterations):
-        start = time.perf_counter()
-        func(data)
-        end = time.perf_counter()
-        times.append(end - start)
-
-    avg = statistics.mean(times)
-    std = statistics.stdev(times) if len(times) > 1 else 0.0
-    return avg, std
-
-
-def _run_benchmarks(
-    benchmarks: Iterable[tuple[str, Callable[[tuple], object]]],
-    data: tuple,
-    iterations: int,
-):
-    """Yield (name, avg, std) for each benchmark, skipping unavailable ones."""
-    for name, func in benchmarks:
-        try:
-            avg, std = _benchmark_func(func, data, iterations)
-        except ModuleNotFoundError as exc:
-            print(f"Skipping {name}: {exc}")
-            continue
-        yield name, avg, std
-
-
-def builtin_hash(data: tuple) -> int:
-    """Wrapper for Python's built-in hash()."""
-    return hash(data)
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--iterations",
-        type=int,
-        default=10_000,
-        help="Number of measured iterations per hash function.",
-    )
-    parser.add_argument(
-        "--seed", type=int, default=42, help="Random seed for test payload."
-    )
-    args = parser.parse_args()
-
-    data = _generate_test_data(args.seed)
-    benchmarks = (
-        ("SHA256 (pickle)", sha256),
-        ("xxHash (pickle)", xxhash),
-        ("built-in hash()", builtin_hash),
-    )
-
-    print("=" * 60)
-    print("HASH FUNCTION MICRO BENCHMARK")
-    print("=" * 60)
-    print("Test data: (32-byte bytes object, 32-int tuple)")
-    print(f"Iterations: {args.iterations:,}")
-    print("=" * 60)
-
-    results = list(_run_benchmarks(benchmarks, data, args.iterations))
-    builtin_entry = next((r for r in results if r[0] == "built-in hash()"), None)
-
-    print("\nResults:")
-    for name, avg, std in results:
-        print(f"  {name:16s}: {avg * 1e6:8.2f} ± {std * 1e6:6.2f} μs")
-
-    if builtin_entry:
-        _, builtin_avg, _ = builtin_entry
-        print("\n" + "=" * 60)
-        print("SUMMARY (relative to built-in hash())")
-        print("=" * 60)
-        for name, avg, _ in results:
-            if name == "built-in hash()":
-                continue
-            speed_ratio = avg / builtin_avg
-            print(f"• {name} is {speed_ratio:.1f}x slower than built-in hash()")
-    else:
-        print("\nBuilt-in hash() result missing; cannot compute speed ratios.")
-
-
-if __name__ == "__main__":
-    main()
--- a/benchmarks/benchmark_ngram_proposer.py
+++ b/benchmarks/benchmark_ngram_proposer.py
@@ -32,11 +32,12 @@ def benchmark_propose(args):

        model_config = ModelConfig(
            model="facebook/opt-125m",
+            task="generate",
            max_model_len=args.num_token + args.num_spec_token,
            tokenizer="facebook/opt-125m",
            tokenizer_mode="auto",
            dtype="auto",
-            seed=0,
+            seed=None,
            trust_remote_code=False,
        )
        proposer = NgramProposer(
@@ -107,10 +108,7 @@ def benchmark_batched_propose(args):
        device_config=DeviceConfig(device=current_platform.device_type),
        parallel_config=ParallelConfig(),
        load_config=LoadConfig(),
-        scheduler_config=SchedulerConfig(
-            max_model_len=model_config.max_model_len,
-            is_encoder_decoder=model_config.is_encoder_decoder,
-        ),
+        scheduler_config=SchedulerConfig(),
    )

    # monkey patch vllm.v1.worker.gpu_model_runner.get_pp_group
@@ -135,6 +133,7 @@ def benchmark_batched_propose(args):
            block_sizes=[16],
        )
        dummy_input_batch._req_ids = list(str(id) for id in range(args.num_req))
+        dummy_input_batch.spec_decode_unsupported_reqs = ()
        dummy_input_batch.num_tokens_no_spec = [args.num_token] * args.num_req
        dummy_input_batch.token_ids_cpu = np.random.randint(
            0, 20, (args.num_req, args.num_token)
@@ -150,8 +149,10 @@ def benchmark_batched_propose(args):
            start = time.time()
            runner.drafter.propose(
                sampled_token_ids,
+                dummy_input_batch.req_ids,
                dummy_input_batch.num_tokens_no_spec,
                dummy_input_batch.token_ids_cpu,
+                dummy_input_batch.spec_decode_unsupported_reqs,
            )
            end = time.time()
            print(f"Iteration time (s): {end - start}")
--- a/benchmarks/benchmark_prefix_block_hash.py
+++ b/benchmarks/benchmark_prefix_block_hash.py
@@ -1,110 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-"""
-Simple benchmark to compare prefix-cache block hashing algorithms.
-
-Example:
-    python benchmark_prefix_block_hash.py --num-blocks 20000 --block-size 32
-"""
-
-from __future__ import annotations
-
-import argparse
-import random
-import statistics
-import sys
-import time
-from collections.abc import Callable, Iterable, Sequence
-
-from vllm.utils.hashing import get_hash_fn_by_name
-from vllm.v1.core.kv_cache_utils import BlockHash, hash_block_tokens, init_none_hash
-
-SUPPORTED_ALGOS = ("sha256", "sha256_cbor", "xxhash", "xxhash_cbor")
-
-
-def _generate_blocks(
-    num_blocks: int, block_size: int, vocab_size: int, seed: int
-) -> list[list[int]]:
-    rng = random.Random(seed)
-    return [
-        [rng.randrange(vocab_size) for _ in range(block_size)]
-        for _ in range(num_blocks)
-    ]
-
-
-def _hash_all_blocks(
-    hash_fn: Callable[[object], bytes],
-    blocks: Iterable[Sequence[int]],
-) -> float:
-    parent_hash: BlockHash | None = None
-    start = time.perf_counter()
-    for block in blocks:
-        parent_hash = hash_block_tokens(hash_fn, parent_hash, block, extra_keys=None)
-    end = time.perf_counter()
-    return end - start
-
-
-def _benchmark(
-    hash_algo: str,
-    blocks: list[list[int]],
-    trials: int,
-) -> tuple[float, float, float] | None:
-    try:
-        hash_fn = get_hash_fn_by_name(hash_algo)
-        init_none_hash(hash_fn)
-        timings = [_hash_all_blocks(hash_fn, blocks) for _ in range(trials)]
-    except ModuleNotFoundError as exc:
-        print(f"Skipping {hash_algo}: {exc}", file=sys.stderr)
-        return None
-
-    avg = statistics.mean(timings)
-    best = min(timings)
-    # throughput: tokens / second
-    tokens_hashed = len(blocks) * len(blocks[0])
-    throughput = tokens_hashed / best
-    return avg, best, throughput
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--num-blocks", type=int, default=10000, help="Block count.")
-    parser.add_argument("--block-size", type=int, default=32, help="Tokens per block.")
-    parser.add_argument(
-        "--vocab-size", type=int, default=32000, help="Token id range [0, vocab_size)."
-    )
-    parser.add_argument("--seed", type=int, default=0, help="Random seed.")
-    parser.add_argument(
-        "--trials", type=int, default=5, help="Number of timed trials per algorithm."
-    )
-    parser.add_argument(
-        "--algorithms",
-        nargs="+",
-        default=SUPPORTED_ALGOS,
-        choices=SUPPORTED_ALGOS,
-        help="Hash algorithms to benchmark.",
-    )
-    args = parser.parse_args()
-
-    blocks = _generate_blocks(
-        args.num_blocks, args.block_size, args.vocab_size, args.seed
-    )
-    print(
-        f"Benchmarking {len(args.algorithms)} algorithms on "
-        f"{args.num_blocks} blocks (block size={args.block_size})."
-    )
-
-    for algo in args.algorithms:
-        result = _benchmark(algo, blocks, args.trials)
-        if result is None:
-            continue
-
-        avg, best, throughput = result
-        print(
-            f"{algo:14s} avg: {avg:.6f}s  best: {best:.6f}s  "
-            f"throughput: {throughput / 1e6:.2f}M tokens/s"
-        )
-
-
-if __name__ == "__main__":
-    main()
--- a/benchmarks/benchmark_prefix_caching.py
+++ b/benchmarks/benchmark_prefix_caching.py
@@ -40,7 +40,7 @@ from vllm.engine.arg_utils import EngineArgs
 from vllm.utils.argparse_utils import FlexibleArgumentParser

 try:
-    from vllm.tokenizers import get_tokenizer
+    from vllm.transformers_utils.tokenizer import get_tokenizer
 except ImportError:
    from backend_request_func import get_tokenizer

--- a/benchmarks/benchmark_serving_structured_output.py
+++ b/benchmarks/benchmark_serving_structured_output.py
@@ -46,7 +46,7 @@ from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase

 try:
-    from vllm.tokenizers import get_tokenizer
+    from vllm.transformers_utils.tokenizer import get_tokenizer
 except ImportError:
    from backend_request_func import get_tokenizer

@@ -574,7 +574,7 @@ async def benchmark(
    )
    print(
        "{:<40} {:<10.2f}".format(
-            "Total token throughput (tok/s):", metrics.total_token_throughput
+            "Total Token throughput (tok/s):", metrics.total_token_throughput
        )
    )

@@ -963,7 +963,8 @@ def create_argument_parser():
    parser.add_argument(
        "--profile",
        action="store_true",
-        help="Use vLLM Profiling. --profiler-config must be provided on the server.",
+        help="Use Torch Profiler. The endpoint must be launched with "
+        "VLLM_TORCH_PROFILER_DIR to enable profiler.",
    )
    parser.add_argument(
        "--result-dir",
--- a/benchmarks/cutlass_benchmarks/sparse_benchmarks.py
+++ b/benchmarks/cutlass_benchmarks/sparse_benchmarks.py
@@ -343,9 +343,7 @@ def bench(
        return bench_int8(dtype, m, k, n, label, sub_label)
    if dtype == torch.float8_e4m3fn:
        return bench_fp8(dtype, m, k, n, label, sub_label)
-    raise ValueError(
-        f"Unsupported dtype {dtype}: should be one of torch.int8, torch.float8_e4m3fn."
-    )
+    raise ValueError("unsupported type")


 # runner
--- a/benchmarks/disagg_benchmarks/disagg_prefill_proxy_server.py
+++ b/benchmarks/disagg_benchmarks/disagg_prefill_proxy_server.py
@@ -5,12 +5,11 @@ import argparse
 import asyncio
 import logging
 import os
-import time
-import uuid
-from urllib.parse import urlparse

 import aiohttp
 from quart import Quart, Response, make_response, request
+from rate_limiter import RateLimiter
+from request_queue import RequestQueue

 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -25,8 +24,26 @@ def parse_args():
    parser.add_argument(
        "--timeout",
        type=float,
-        default=6 * 60 * 60,
-        help="Timeout for backend service requests in seconds (default: 21600)",
+        default=300,
+        help="Timeout for backend service requests in seconds (default: 300)",
+    )
+    parser.add_argument(
+        "--max-concurrent",
+        type=int,
+        default=100,
+        help="Maximum concurrent requests to backend services (default: 100)",
+    )
+    parser.add_argument(
+        "--queue-size",
+        type=int,
+        default=500,
+        help="Maximum number of requests in the queue (default: 500)",
+    )
+    parser.add_argument(
+        "--rate-limit",
+        type=int,
+        default=40,
+        help="Maximum requests per second (default: 40)",
    )
    parser.add_argument(
        "--port",
@@ -37,32 +54,14 @@ def parse_args():
    parser.add_argument(
        "--prefill-url",
        type=str,
-        default="http://localhost:8100",
-        help="Prefill service base URL (protocol + host[:port])",
+        default="http://localhost:8100/v1/completions",
+        help="Prefill service endpoint URL",
    )
    parser.add_argument(
        "--decode-url",
        type=str,
-        default="http://localhost:8200",
-        help="Decode service base URL (protocol + host[:port])",
-    )
-    parser.add_argument(
-        "--kv-host",
-        type=str,
-        default="localhost",
-        help="Hostname or IP used by KV transfer (default: localhost)",
-    )
-    parser.add_argument(
-        "--prefill-kv-port",
-        type=int,
-        default=14579,
-        help="Prefill KV port (default: 14579)",
-    )
-    parser.add_argument(
-        "--decode-kv-port",
-        type=int,
-        default=14580,
-        help="Decode KV port (default: 14580)",
+        default="http://localhost:8200/v1/completions",
+        help="Decode service endpoint URL",
    )

    return parser.parse_args()
@@ -74,129 +73,70 @@ def main():

    # Initialize configuration using command line parameters
    AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=args.timeout)
+    MAX_CONCURRENT_REQUESTS = args.max_concurrent
+    REQUEST_QUEUE_SIZE = args.queue_size
+    RATE_LIMIT = args.rate_limit
    PREFILL_SERVICE_URL = args.prefill_url
    DECODE_SERVICE_URL = args.decode_url
    PORT = args.port

-    PREFILL_KV_ADDR = f"{args.kv_host}:{args.prefill_kv_port}"
-    DECODE_KV_ADDR = f"{args.kv_host}:{args.decode_kv_port}"
-
-    logger.info(
-        "Proxy resolved KV addresses -> prefill: %s, decode: %s",
-        PREFILL_KV_ADDR,
-        DECODE_KV_ADDR,
-    )
-
    app = Quart(__name__)

-    # Attach the configuration object to the application instance so helper
-    # coroutines can read the resolved backend URLs and timeouts without using
-    # globals.
+    # Initialize the rate limiter and request queue
+    rate_limiter = RateLimiter(RATE_LIMIT)
+    request_queue = RequestQueue(MAX_CONCURRENT_REQUESTS, REQUEST_QUEUE_SIZE)
+
+    # Attach the configuration object to the application instance
    app.config.update(
        {
            "AIOHTTP_TIMEOUT": AIOHTTP_TIMEOUT,
+            "rate_limiter": rate_limiter,
+            "request_queue": request_queue,
            "PREFILL_SERVICE_URL": PREFILL_SERVICE_URL,
            "DECODE_SERVICE_URL": DECODE_SERVICE_URL,
-            "PREFILL_KV_ADDR": PREFILL_KV_ADDR,
-            "DECODE_KV_ADDR": DECODE_KV_ADDR,
        }
    )

-    def _normalize_base_url(url: str) -> str:
-        """Remove any trailing slash so path joins behave predictably."""
-        return url.rstrip("/")
+    # Start queue processing on app startup
+    @app.before_serving
+    async def startup():
+        """Start request processing task when app starts serving"""
+        asyncio.create_task(request_queue.process())

-    def _get_host_port(url: str) -> str:
-        """Return the hostname:port portion for logging and KV headers."""
-        parsed = urlparse(url)
-        host = parsed.hostname or "localhost"
-        port = parsed.port
-        if port is None:
-            port = 80 if parsed.scheme == "http" else 443
-        return f"{host}:{port}"
+    async def forward_request(url, data):
+        """Forward request to backend service with rate limiting and error handling"""
+        headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}

-    PREFILL_BASE = _normalize_base_url(PREFILL_SERVICE_URL)
-    DECODE_BASE = _normalize_base_url(DECODE_SERVICE_URL)
-    KV_TARGET = _get_host_port(DECODE_SERVICE_URL)
-
-    def _build_headers(request_id: str) -> dict[str, str]:
-        """Construct the headers expected by vLLM's P2P disagg connector."""
-        headers: dict[str, str] = {"X-Request-Id": request_id, "X-KV-Target": KV_TARGET}
-        api_key = os.environ.get("OPENAI_API_KEY")
-        if api_key:
-            headers["Authorization"] = f"Bearer {api_key}"
-        return headers
-
-    async def _run_prefill(
-        request_path: str,
-        payload: dict,
-        headers: dict[str, str],
-        request_id: str,
-    ):
-        url = f"{PREFILL_BASE}{request_path}"
-        start_ts = time.perf_counter()
-        logger.info("[prefill] start request_id=%s url=%s", request_id, url)
-        try:
-            async with (
-                aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session,
-                session.post(url=url, json=payload, headers=headers) as resp,
-            ):
-                if resp.status != 200:
-                    error_text = await resp.text()
-                    raise RuntimeError(
-                        f"Prefill backend error {resp.status}: {error_text}"
-                    )
-                await resp.read()
-                logger.info(
-                    "[prefill] done request_id=%s status=%s elapsed=%.2fs",
-                    request_id,
-                    resp.status,
-                    time.perf_counter() - start_ts,
-                )
-        except asyncio.TimeoutError as exc:
-            raise RuntimeError(f"Prefill service timeout at {url}") from exc
-        except aiohttp.ClientError as exc:
-            raise RuntimeError(f"Prefill service unavailable at {url}") from exc
-
-    async def _stream_decode(
-        request_path: str,
-        payload: dict,
-        headers: dict[str, str],
-        request_id: str,
-    ):
-        url = f"{DECODE_BASE}{request_path}"
-        # Stream tokens from the decode service once the prefill stage has
-        # materialized KV caches on the target workers.
-        logger.info("[decode] start request_id=%s url=%s", request_id, url)
-        try:
-            async with (
-                aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session,
-                session.post(url=url, json=payload, headers=headers) as resp,
-            ):
-                if resp.status != 200:
-                    error_text = await resp.text()
-                    logger.error(
-                        "Decode backend error %s - %s", resp.status, error_text
-                    )
-                    err_msg = (
-                        '{"error": "Decode backend error ' + str(resp.status) + '"}'
-                    )
-                    yield err_msg.encode()
-                    return
-                logger.info(
-                    "[decode] streaming response request_id=%s status=%s",
-                    request_id,
-                    resp.status,
-                )
-                async for chunk_bytes in resp.content.iter_chunked(1024):
-                    yield chunk_bytes
-                logger.info("[decode] finished streaming request_id=%s", request_id)
-        except asyncio.TimeoutError:
-            logger.error("Decode service timeout at %s", url)
-            yield b'{"error": "Decode service timeout"}'
-        except aiohttp.ClientError as exc:
-            logger.error("Decode service error at %s: %s", url, exc)
-            yield b'{"error": "Decode service unavailable"}'
+        # Use rate limiter as context manager
+        async with (
+            rate_limiter,
+            aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session,
+        ):
+            try:
+                async with session.post(
+                    url=url, json=data, headers=headers
+                ) as response:
+                    if response.status == 200:
+                        # Stream response chunks
+                        async for chunk_bytes in response.content.iter_chunked(1024):
+                            yield chunk_bytes
+                    else:
+                        # Handle backend service errors
+                        error_text = await response.text()
+                        logger.error(
+                            "Backend service error: %s - %s",
+                            response.status,
+                            error_text,
+                        )
+                        yield b'{"error": "Backend service error"}'
+            except aiohttp.ClientError as e:
+                # Handle connection errors
+                logger.error("Connection error to %s: %s", url, str(e))
+                yield b'{"error": "Service unavailable"}'
+            except asyncio.TimeoutError:
+                # Handle timeout errors
+                logger.error("Timeout connecting to %s", url)
+                yield b'{"error": "Service timeout"}'

    async def process_request():
        """Process a single request through prefill and decode stages"""
@@ -206,27 +146,13 @@ def main():
            # Create prefill request (max_tokens=1)
            prefill_request = original_request_data.copy()
            prefill_request["max_tokens"] = 1
-            if "max_completion_tokens" in prefill_request:
-                prefill_request["max_completion_tokens"] = 1

            # Execute prefill stage
-            # The request id encodes both KV socket addresses so the backend can
-            # shuttle tensors directly via NCCL once the prefill response
-            # completes.
-            request_id = (
-                f"___prefill_addr_{PREFILL_KV_ADDR}___decode_addr_"
-                f"{DECODE_KV_ADDR}_{uuid.uuid4().hex}"
-            )
-
-            headers = _build_headers(request_id)
-            await _run_prefill(request.path, prefill_request, headers, request_id)
+            async for _ in forward_request(PREFILL_SERVICE_URL, prefill_request):
+                continue

            # Execute decode stage and stream response
-            # Pass the unmodified user request so the decode phase can continue
-            # sampling with the already-populated KV cache.
-            generator = _stream_decode(
-                request.path, original_request_data, headers, request_id
-            )
+            generator = forward_request(DECODE_SERVICE_URL, original_request_data)
            response = await make_response(generator)
            response.timeout = None  # Disable timeout for streaming response
            return response
@@ -242,10 +168,23 @@ def main():
    @app.route("/v1/completions", methods=["POST"])
    async def handle_request():
        """Handle incoming API requests with concurrency and rate limiting"""
+        # Create task for request processing
+        task = asyncio.create_task(process_request())
+
+        # Enqueue request or reject if queue is full
+        if not await request_queue.enqueue(task):
+            return Response(
+                response=b'{"error": "Server busy, try again later"}',
+                status=503,
+                content_type="application/json",
+            )
+
        try:
-            return await process_request()
+            # Return the response from the processing task
+            return await task
        except asyncio.CancelledError:
-            logger.warning("Request cancelled")
+            # Handle task cancellation (timeout or queue full)
+            logger.warning("Request cancelled due to timeout or queue full")
            return Response(
                response=b'{"error": "Request cancelled"}',
                status=503,
--- a/benchmarks/fused_kernels/layernorm_rms_benchmarks.py
+++ b/benchmarks/fused_kernels/layernorm_rms_benchmarks.py
@@ -14,9 +14,6 @@ from tqdm import tqdm

 import vllm._custom_ops as ops
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    per_token_group_quant_fp8,
-)


@dataclass
@@ -25,7 +22,6 @@ class bench_params_t:
    hidden_size: int
    add_residual: bool
    dtype: torch.dtype
-    group_size: list[int]

    def description(self):
        return (
@@ -33,7 +29,6 @@ class bench_params_t:
            f"x D {self.hidden_size} "
            f"x R {self.add_residual} "
            f"x DT {self.dtype}"
-            f"x GS {self.group_size}"
        )


@@ -43,11 +38,10 @@ def get_bench_params() -> list[bench_params_t]:
    HIDDEN_SIZES = list(range(1024, 8129, 1024))
    ADD_RESIDUAL = [True, False]
    DTYPES = [torch.bfloat16, torch.float]
-    GROUP_SIZES = [[1, 64], [1, 128]]

-    combinations = product(NUM_TOKENS, HIDDEN_SIZES, ADD_RESIDUAL, DTYPES, GROUP_SIZES)
+    combinations = product(NUM_TOKENS, HIDDEN_SIZES, ADD_RESIDUAL, DTYPES)
    bench_params = list(
-        map(lambda x: bench_params_t(x[0], x[1], x[2], x[3], x[4]), combinations)
+        map(lambda x: bench_params_t(x[0], x[1], x[2], x[3]), combinations)
    )
    return bench_params

@@ -58,7 +52,6 @@ def unfused_int8_impl(
    x: torch.Tensor,
    residual: torch.Tensor | None,
    quant_dtype: torch.dtype,
-    group_size: list[int],
 ):
    # Norm
    torch_out = None
@@ -76,7 +69,6 @@ def unfused_fp8_impl(
    x: torch.Tensor,
    residual: torch.Tensor | None,
    quant_dtype: torch.dtype,
-    group_size: list[int],
 ):
    # Norm
    torch_out = None
@@ -89,63 +81,23 @@ def unfused_fp8_impl(
    torch_out, _ = ops.scaled_fp8_quant(torch_out)


-def unfused_groupwise_fp8_impl(
-    rms_norm_layer: RMSNorm,
-    x: torch.Tensor,
-    residual: torch.Tensor | None,
-    quant_dtype: torch.dtype,
-    group_size: list[int],
-):
-    # Norm
-    torch_out = None
-    if residual is None:
-        torch_out = rms_norm_layer.forward_cuda(x, residual)
-    else:
-        torch_out, _ = rms_norm_layer.forward_cuda(x, residual)
-
-    # Quant
-    torch_out, _ = per_token_group_quant_fp8(
-        torch_out, group_size=group_size[1], use_ue8m0=False
-    )
-
-
 def fused_impl(
    rms_norm_layer: RMSNorm,  # this stores the weights
    x: torch.Tensor,
    residual: torch.Tensor | None,
    quant_dtype: torch.dtype,
-    group_size: list[int],
 ):
    out, _ = ops.rms_norm_dynamic_per_token_quant(
        x, rms_norm_layer.weight, 1e-6, quant_dtype, residual=residual
    )


-def fused_groupwise_impl(
-    rms_norm_layer: RMSNorm,  # this stores the weights
-    x: torch.Tensor,
-    residual: torch.Tensor | None,
-    quant_dtype: torch.dtype,
-    group_size: list[int],
-):
-    out, _ = ops.rms_norm_per_block_quant(
-        x,
-        rms_norm_layer.weight,
-        1e-6,
-        quant_dtype,
-        group_size,
-        residual=residual,
-        is_scale_transposed=True,
-    )
-
-
 # Bench functions
 def bench_fn(
    rms_norm_layer: RMSNorm,
    x: torch.Tensor,
    residual: torch.Tensor,
    quant_dtype: torch.dtype,
-    group_size: list[int],
    label: str,
    sub_label: str,
    fn: Callable,
@@ -158,11 +110,10 @@ def bench_fn(
        "x": x,
        "residual": residual,
        "quant_dtype": quant_dtype,
-        "group_size": group_size,
        "fn": fn,
    }
    return TBenchmark.Timer(
-        stmt="fn(rms_norm_layer, x, residual, quant_dtype, group_size)",
+        stmt="fn(rms_norm_layer, x, residual, quant_dtype)",
        globals=globals,
        label=label,
        sub_label=sub_label,
@@ -196,7 +147,6 @@ def bench(params: bench_params_t, label: str, sub_label: str) -> Iterable[TMeasu
            x,
            residual,
            torch.int8,
-            params.group_size,
            label,
            sub_label,
            unfused_int8_impl,
@@ -211,7 +161,6 @@ def bench(params: bench_params_t, label: str, sub_label: str) -> Iterable[TMeasu
            x,
            residual,
            torch.float8_e4m3fn,
-            params.group_size,
            label,
            sub_label,
            unfused_fp8_impl,
@@ -226,7 +175,6 @@ def bench(params: bench_params_t, label: str, sub_label: str) -> Iterable[TMeasu
            x,
            residual,
            torch.int8,
-            params.group_size,
            label,
            sub_label,
            fused_impl,
@@ -241,7 +189,6 @@ def bench(params: bench_params_t, label: str, sub_label: str) -> Iterable[TMeasu
            x,
            residual,
            torch.float8_e4m3fn,
-            params.group_size,
            label,
            sub_label,
            fused_impl,
@@ -249,36 +196,6 @@ def bench(params: bench_params_t, label: str, sub_label: str) -> Iterable[TMeasu
        )
    )

-    # unfused groupwise fp8 impl.
-    timers.append(
-        bench_fn(
-            layer,
-            x,
-            residual,
-            torch.float8_e4m3fn,
-            params.group_size,
-            label,
-            sub_label,
-            unfused_groupwise_fp8_impl,
-            "unfused_groupwise_fp8_impl",
-        )
-    )
-
-    # fused groupwise fp8 impl.
-    timers.append(
-        bench_fn(
-            layer,
-            x,
-            residual,
-            torch.float8_e4m3fn,
-            params.group_size,
-            label,
-            sub_label,
-            fused_groupwise_impl,
-            "fused_groupwise_fp8_impl",
-        )
-    )
-
    print_timers(timers)

    return timers
--- a/benchmarks/kernels/bench_nvfp4_quant.py
+++ b/benchmarks/kernels/bench_nvfp4_quant.py
@@ -1,177 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import argparse
-import copy
-import itertools
-
-import torch
-from weight_shapes import WEIGHT_SHAPES
-
-from vllm import _custom_ops as ops
-from vllm.platforms import current_platform
-from vllm.scalar_type import scalar_types
-from vllm.triton_utils import triton
-from vllm.utils.flashinfer import flashinfer_fp4_quantize
-
-if not current_platform.has_device_capability(100):
-    raise RuntimeError("NVFP4 requires compute capability of 10.0 (Blackwell)")
-
-FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
-FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
-
-PROVIDER_CFGS = {
-    "vllm": dict(backend="vllm", enabled=True),
-    "flashinfer": dict(backend="flashinfer", enabled=True),
-}
-
-_enabled = [k for k, v in PROVIDER_CFGS.items() if v["enabled"]]
-
-
-def compute_global_scale(tensor: torch.Tensor) -> torch.Tensor:
-    """Compute global scale for FP4 quantization."""
-    amax = torch.abs(tensor).max().to(torch.float32)
-    return FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / amax
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=["batch_size"],
-        x_vals=[1, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096],
-        x_log=False,
-        line_arg="provider",
-        line_vals=_enabled,
-        line_names=_enabled,
-        ylabel="us (lower is better)",
-        plot_name="NVFP4 Input Quantization Latency (us)",
-        args={},
-    )
-)
-def benchmark(batch_size, provider, N, K):
-    M = batch_size
-    device = "cuda"
-    dtype = torch.bfloat16
-
-    # Create input tensor
-    a = torch.randn((M, K), device=device, dtype=dtype)
-
-    # Compute global scale for activation
-    a_global_scale = compute_global_scale(a)
-
-    quantiles = [0.5, 0.2, 0.8]
-
-    cfg = PROVIDER_CFGS[provider]
-
-    if cfg["backend"] == "vllm":
-        # vLLM's FP4 quantization
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: ops.scaled_fp4_quant(a, a_global_scale),
-            quantiles=quantiles,
-        )
-    elif cfg["backend"] == "flashinfer":
-        # FlashInfer's FP4 quantization
-        # Use is_sf_swizzled_layout=True to match vLLM's output format
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: flashinfer_fp4_quantize(
-                a, a_global_scale, is_sf_swizzled_layout=True
-            ),
-            quantiles=quantiles,
-        )
-
-    # Convert ms to us for better readability at small batch sizes
-    to_us = lambda t_ms: t_ms * 1000
-    return to_us(ms), to_us(max_ms), to_us(min_ms)
-
-
-def prepare_shapes(args):
-    out = []
-    for model, tp_size in itertools.product(args.models, args.tp_sizes):
-        for KN, tp_dim in copy.deepcopy(WEIGHT_SHAPES[model]):
-            KN[tp_dim] //= tp_size
-            KN.append(model)
-            out.append(KN)
-    return out
-
-
-def _test_accuracy_once(M: int, K: int, dtype: torch.dtype, device: str):
-    """Test accuracy between vLLM and FlashInfer FP4 quantization."""
-    # Create input tensor
-    a = torch.randn((M, K), device=device, dtype=dtype)
-
-    # Compute global scale
-    a_global_scale = compute_global_scale(a)
-
-    # vLLM quantization
-    vllm_fp4, vllm_scale = ops.scaled_fp4_quant(a, a_global_scale)
-
-    # FlashInfer quantization (with swizzled layout to match vLLM's output)
-    flashinfer_fp4, flashinfer_scale = flashinfer_fp4_quantize(
-        a, a_global_scale, is_sf_swizzled_layout=True
-    )
-    flashinfer_scale = flashinfer_scale.view(torch.float8_e4m3fn)
-
-    # Compare outputs
-    torch.testing.assert_close(
-        vllm_fp4,
-        flashinfer_fp4,
-    )
-    print(f"M={M}, K={K}, dtype={dtype}: PASSED")
-
-
-def test_accuracy():
-    """Run accuracy tests across various shapes."""
-    print("\n" + "=" * 60)
-    print("Running accuracy tests: vLLM vs FlashInfer")
-    print("=" * 60)
-
-    device = "cuda"
-    dtype = torch.bfloat16
-
-    # Test various batch sizes and hidden dimensions
-    Ms = [1, 1024]
-    Ks = [4096]
-
-    for M in Ms:
-        for K in Ks:
-            _test_accuracy_once(M, K, dtype, device)
-
-    print("\nAll accuracy tests passed!")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Benchmark NVFP4 quantization: vLLM vs FlashInfer"
-    )
-    parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=["meta-llama/Llama-3.1-8B-Instruct"],
-        choices=list(WEIGHT_SHAPES.keys()),
-    )
-    parser.add_argument("--tp-sizes", nargs="+", type=int, default=[1])
-    parser.add_argument(
-        "--save-path",
-        type=str,
-        default=None,
-        help="Path to save benchmark results",
-    )
-    parser.add_argument(
-        "--accuracy",
-        action="store_true",
-        help="Run accuracy tests",
-    )
-    args = parser.parse_args()
-
-    if args.accuracy:
-        test_accuracy()
-
-    for K, N, model in prepare_shapes(args):
-        print(f"\n{model}, N={N} K={K}")
-        benchmark.run(
-            print_data=True,
-            save_path=args.save_path,
-            N=N,
-            K=K,
-        )
-
-    print("\nBenchmark finished!")
--- a/benchmarks/kernels/benchmark_2d_silu_mul_fp8_quant.py
+++ b/benchmarks/kernels/benchmark_2d_silu_mul_fp8_quant.py
@@ -1,244 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from dataclasses import dataclass
-from enum import Enum
-from itertools import product
-from typing import Any
-
-import torch
-import torch.utils.benchmark as TBenchmark
-from torch.utils.benchmark import Measurement as TMeasurement
-
-from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    _per_token_group_quant_fp8_colmajor,
-    silu_mul_per_token_group_quant_fp8_colmajor,
-)
-from vllm.triton_utils import triton
-from vllm.utils.deep_gemm import is_deep_gemm_e8m0_used
-
-from .utils import ArgPool, Bench, CudaGraphBenchParams
-
-GROUP_SIZE = 128
-FLOAT8_T = torch.float8_e4m3fn
-
-
-def print_timers(timers: list[TMeasurement], cuda_graph_nops: int):
-    print(
-        f"Note : The timings reported above is for {cuda_graph_nops} "
-        "consecutive invocations of the benchmarking functions. "
-        f"Please divide by {cuda_graph_nops} for single invocation "
-        "timings."
-    )
-    compare = TBenchmark.Compare(timers)
-    compare.print()
-
-
-class ImplType(Enum):
-    SILU_MUL_PER_TOKEN_GROUP_QUANT_FP8_COLMAJOR = 1
-    REFERENCE = 2
-
-    def get_impl(self):
-        if self == ImplType.SILU_MUL_PER_TOKEN_GROUP_QUANT_FP8_COLMAJOR:
-            return silu_mul_per_token_group_quant_fp8_colmajor
-        elif self == ImplType.REFERENCE:
-            return reference
-        raise ValueError(f"Unrecognized ImplType {self}")
-
-
-@dataclass
-class BenchmarkTensors:
-    input: torch.Tensor
-    output: torch.Tensor
-
-    # Reference act output tensor
-    ref_act_out: torch.Tensor
-    ref_quant_out: torch.Tensor
-
-    @staticmethod
-    def make(T: int, N: int) -> "BenchmarkTensors":
-        assert T % GROUP_SIZE == 0
-        assert N % (GROUP_SIZE * 2) == 0
-
-        input = torch.rand((T, N), dtype=torch.bfloat16, device="cuda")
-
-        # silu_mul_per_token_group_quant_fp8_colmajor output.
-        output = torch.rand((T, N // 2), dtype=torch.bfloat16, device="cuda").to(
-            FLOAT8_T
-        )
-
-        # reference output.
-        ref_act_out = torch.empty((T, N // 2), dtype=torch.bfloat16, device="cuda")
-        ref_quant_out = torch.empty(
-            (T, N // 2), dtype=torch.bfloat16, device="cuda"
-        ).to(FLOAT8_T)
-
-        return BenchmarkTensors(
-            input=input,
-            output=output,
-            ref_act_out=ref_act_out,
-            ref_quant_out=ref_quant_out,
-        )
-
-    @property
-    def T(self):
-        return self.input.size(0)
-
-    @property
-    def N(self):
-        return self.input.size(1)
-
-    def make_impl_kwargs(self, impl_type: ImplType) -> dict[str, Any]:
-        if impl_type == ImplType.SILU_MUL_PER_TOKEN_GROUP_QUANT_FP8_COLMAJOR:
-            return {
-                "input": self.input,
-                "output": self.output,
-                "use_ue8m0": is_deep_gemm_e8m0_used(),
-            }
-        elif impl_type == ImplType.REFERENCE:
-            return {
-                "input": self.input,
-                "act_out": self.ref_act_out,
-                "quant_out": self.ref_quant_out,
-                "use_ue8m0": is_deep_gemm_e8m0_used(),
-            }
-        raise ValueError(f"Unrecognized impl_type {impl_type}")
-
-
-def reference_quant(x: torch.Tensor, quant_out: torch.Tensor, use_ue8m0: bool):
-    """
-    Reference triton quant kernel from,
-    vllm.model_executor.layers.quantization.utils.fp8_utils
-    """
-    assert quant_out.size() == x.size()
-    # Allocate the scale tensor column-major format.
-    shape = (x.shape[-1] // GROUP_SIZE,) + x.shape[:-1]
-    x_q = quant_out
-    x_s = torch.empty(shape, device=x.device, dtype=torch.float32).permute(-1, -2)
-
-    M = x.numel() // GROUP_SIZE
-    N = GROUP_SIZE
-    BLOCK = triton.next_power_of_2(N)
-    # heuristics for number of warps
-    num_warps = min(max(BLOCK // 256, 1), 8)
-    num_stages = 1
-
-    finfo = torch.finfo(FLOAT8_T)
-    fp8_min = finfo.min
-    fp8_max = finfo.max
-
-    _per_token_group_quant_fp8_colmajor[(M,)](
-        x,
-        x_q,
-        x_s,
-        GROUP_SIZE,
-        x.shape[1],
-        x.stride(0),
-        x_s.stride(1),
-        eps=1e-10,
-        fp8_min=fp8_min,
-        fp8_max=fp8_max,
-        use_ue8m0=use_ue8m0,
-        BLOCK=BLOCK,
-        num_warps=num_warps,
-        num_stages=num_stages,
-    )
-    return x_q, x_s
-
-
-def reference(
-    input: torch.Tensor,
-    act_out: torch.Tensor,
-    quant_out: torch.Tensor,
-    use_ue8m0: bool,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    torch.ops._C.silu_and_mul(act_out, input)
-    return reference_quant(act_out, quant_out, use_ue8m0)
-
-
-def bench_impl(
-    bench_tensors: list[BenchmarkTensors], impl_type: ImplType
-) -> TMeasurement:
-    T = bench_tensors[0].T
-    N = bench_tensors[0].N
-
-    arg_pool_size = len(bench_tensors)
-    kwargs_list = [bt.make_impl_kwargs(impl_type) for bt in bench_tensors]
-
-    # warmup
-    for kwargs in kwargs_list:
-        impl_type.get_impl()(**kwargs)
-    torch.cuda.synchronize()
-
-    # Merge into a single kwargs and qualify arguments as ArgPool
-    kwargs = {k: ArgPool([]) for k in kwargs_list[0]}
-    for _kwargs in kwargs_list:
-        for k, v in _kwargs.items():
-            kwargs[k].values.append(v)
-
-    cuda_graph_params = None
-    cuda_graph_params = CudaGraphBenchParams(arg_pool_size)
-    timer = None
-    with Bench(
-        cuda_graph_params,
-        "silu-mul-quant",
-        f"num_tokens={T}, N={N}",
-        impl_type.name,
-        impl_type.get_impl(),
-        **kwargs,
-    ) as bench:
-        timer = bench.run()
-    return timer
-
-
-def test_correctness(T: int, N: int):
-    print(f"Testing num_tokens={T}, N={N} ...")
-
-    bench_tensor = BenchmarkTensors.make(T, N)
-
-    def output_from_impl(impl: ImplType) -> tuple[torch.Tensor, torch.Tensor]:
-        return impl.get_impl()(**bench_tensor.make_impl_kwargs(impl))
-
-    # reference output
-    ref_out_q, ref_out_s = output_from_impl(ImplType.REFERENCE)
-
-    # test ouptut
-    out_q, out_s = output_from_impl(
-        ImplType.SILU_MUL_PER_TOKEN_GROUP_QUANT_FP8_COLMAJOR
-    )
-
-    torch.testing.assert_close(ref_out_q.to(torch.float32), out_q.to(torch.float32))
-    torch.testing.assert_close(ref_out_s, out_s)
-
-
-def run(Ts: list[int], Ns: list[int], arg_pool_size: int) -> list[TMeasurement]:
-    timers = []
-    for N, T in product(Ns, Ts):
-        test_correctness(T, N)
-
-        bench_tensors: list[BenchmarkTensors] = [
-            BenchmarkTensors.make(T, N) for _ in range(arg_pool_size)
-        ]
-
-        silu_mul_quant_timer = bench_impl(
-            bench_tensors, ImplType.SILU_MUL_PER_TOKEN_GROUP_QUANT_FP8_COLMAJOR
-        )
-        timers.append(silu_mul_quant_timer)
-        reference_timer = bench_impl(bench_tensors, ImplType.REFERENCE)
-        timers.append(reference_timer)
-
-        print_timers(
-            [silu_mul_quant_timer, reference_timer], cuda_graph_nops=arg_pool_size
-        )
-
-    print_timers(timers, cuda_graph_nops=arg_pool_size)
-
-    return timers
-
-
-if __name__ == "__main__":
-    T = [128 * i for i in range(1, 16)] + [2048 * i for i in range(1, 65)]
-    N = [2048, 4096, 8192]
-
-    print(f"T = {T}, N = {N}")
-    run(T, N, arg_pool_size=8)
--- a/benchmarks/kernels/benchmark_activation.py
+++ b/benchmarks/kernels/benchmark_activation.py
@@ -8,12 +8,13 @@ import torch

 import vllm.model_executor.layers.activation  # noqa F401
 from vllm.model_executor.custom_op import CustomOp
+from vllm.platforms import current_platform
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE

-batch_size_range = [1, 16, 128]
-seq_len_range = [1, 16, 64, 1024, 4096]
+batch_size_range = [1, 16, 32, 64, 128]
+seq_len_range = [1, 16, 64, 128, 256, 512, 1024, 2048, 4096]
 intermediate_size = [3072, 9728, 12288]
 configs = list(itertools.product(batch_size_range, seq_len_range, intermediate_size))

@@ -29,7 +30,7 @@ def benchmark_activation(
    device = "cuda"
    num_tokens = batch_size * seq_len
    dim = intermediate_size
-    set_random_seed(42)
+    current_platform.seed_everything(42)
    torch.set_default_device(device)

    if func_name == "gelu_and_mul":
--- a/benchmarks/kernels/benchmark_cutlass_moe_nvfp4.py
+++ b/benchmarks/kernels/benchmark_cutlass_moe_nvfp4.py
@@ -11,23 +11,16 @@ import nvtx
 import torch
 import torch.utils.benchmark as benchmark

-import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.fused_moe.config import (
    fp8_w8a8_moe_quant_config,
    nvfp4_moe_quant_config,
 )
-from vllm.model_executor.layers.fused_moe.cutlass_moe import (
-    CutlassExpertsFp4,
-)
+from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp4
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
-from vllm.model_executor.layers.fused_moe.prepare_finalize import (
-    MoEPrepareAndFinalizeNoEP,
-)
 from vllm.scalar_type import scalar_types
 from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.v1.worker.workspace import init_workspace_manager

 WEIGHT_SHAPES_MOE = {
    "nvidia/DeepSeek-R1-FP4": [
@@ -194,24 +187,19 @@ def bench_run(
            g1_alphas=w1_gs,
            g2_alphas=w2_gs,
        )
-
-        kernel = mk.FusedMoEModularKernel(
-            MoEPrepareAndFinalizeNoEP(defer_input_quant=True),
-            CutlassExpertsFp4(
-                out_dtype=dtype,
-                max_experts_per_worker=e,
-                quant_config=quant_config,
-            ),
-        )
-
        for _ in range(num_repeats):
            with nvtx.annotate("cutlass_moe_fp4", color="green"):
-                kernel(
-                    hidden_states=a,
-                    w1=w1_fp4,
-                    w2=w2_fp4,
+                cutlass_moe_fp4(
+                    a=a,
+                    w1_fp4=w1_fp4,
+                    w2_fp4=w2_fp4,
                    topk_weights=topk_weights,
                    topk_ids=topk_ids,
+                    m=m,
+                    n=n,
+                    k=k,
+                    e=num_experts,
+                    quant_config=quant_config,
                )

    def run_cutlass_from_graph(
@@ -241,24 +229,20 @@ def bench_run(
            g2_alphas=w2_gs,
        )

-        kernel = mk.FusedMoEModularKernel(
-            MoEPrepareAndFinalizeNoEP(defer_input_quant=True),
-            CutlassExpertsFp4(
-                out_dtype=dtype,
-                max_experts_per_worker=e,
-                quant_config=quant_config,
-            ),
-        )
-
        with set_current_vllm_config(
            VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
        ):
-            return kernel(
-                hidden_states=a,
-                w1=w1_fp4,
-                w2=w2_fp4,
+            return cutlass_moe_fp4(
+                a=a,
+                w1_fp4=w1_fp4,
+                w2_fp4=w2_fp4,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
+                m=m,
+                n=n,
+                k=k,
+                e=num_experts,
+                quant_config=quant_config,
            )

    def run_triton_from_graph(
@@ -457,10 +441,6 @@ def bench_run(


 def main(args):
-    # Initialize workspace manager (required for CUTLASS MoE kernels)
-    device = torch.device("cuda:0")
-    init_workspace_manager(device)
-
    print("Benchmarking models:")
    for i, model in enumerate(args.models):
        print(f"[{i}]  {model}")
--- a/benchmarks/kernels/benchmark_cutlass_moe_fp8.py
+++ b/benchmarks/kernels/benchmark_cutlass_moe_fp8.py
@@ -6,19 +6,15 @@ kernel. Both kernels take in fp8 quantized weights and 16-bit activations,
 but use different quantization strategies and backends.
 """

+import nvtx
 import torch

-import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
-from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
+from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp8
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
-from vllm.model_executor.layers.fused_moe.prepare_finalize import (
-    MoEPrepareAndFinalizeNoEP,
-)
 from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.v1.worker.workspace import init_workspace_manager

 # Weight shapes for different models: [num_experts, topk, hidden_size,
 # intermediate_size]
@@ -62,7 +58,6 @@ def bench_run(
    per_out_ch: bool,
    mkn: tuple[int, int, int],
 ):
-    init_workspace_manager(torch.cuda.current_device())
    (m, k, n) = mkn

    dtype = torch.half
@@ -125,6 +120,85 @@ def bench_run(
    # Force per-tensor quantization for all cases
    per_act_token = False

+    # Create stride tensors for CUTLASS
+    ab_strides1 = torch.full((num_experts,), k, dtype=torch.int64, device=device)
+    ab_strides2 = torch.full((num_experts,), n, dtype=torch.int64, device=device)
+    c_strides1 = torch.full((num_experts,), 2 * n, dtype=torch.int64, device=device)
+    c_strides2 = torch.full((num_experts,), k, dtype=torch.int64, device=device)
+
+    def run_triton_moe(
+        a: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        a1_scale: torch.Tensor,
+        a2_scale: torch.Tensor,
+        num_repeats: int,
+    ):
+        quant_config = fp8_w8a8_moe_quant_config(
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+            per_act_token_quant=per_act_token,
+            per_out_ch_quant=per_out_ch,
+        )
+
+        for _ in range(num_repeats):
+            fused_experts(
+                a,
+                w1,
+                w2,
+                topk_weights,
+                topk_ids,
+                quant_config=quant_config,
+            )
+
+    def run_cutlass_moe_fp8(
+        a: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        ab_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides1: torch.Tensor,
+        c_strides2: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        a1_scale: torch.Tensor,
+        a2_scale: torch.Tensor,
+        num_repeats: int,
+    ):
+        quant_config = fp8_w8a8_moe_quant_config(
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+            per_act_token_quant=per_act_token,
+            per_out_ch_quant=per_out_ch,
+        )
+
+        for _ in range(num_repeats):
+            with nvtx.annotate("cutlass_moe_fp8", color="blue"):
+                cutlass_moe_fp8(
+                    a=a,
+                    w1_q=w1,
+                    w2_q=w2,
+                    topk_weights=topk_weights,
+                    topk_ids=topk_ids,
+                    ab_strides1=ab_strides1,
+                    ab_strides2=ab_strides2,
+                    c_strides1=c_strides1,
+                    c_strides2=c_strides2,
+                    quant_config=quant_config,
+                    activation="silu",
+                    global_num_experts=num_experts,
+                )
+
    # Pre-create quantization config to avoid creating it inside CUDA graph
    quant_config = fp8_w8a8_moe_quant_config(
        w1_scale=w1_scale,
@@ -135,30 +209,23 @@ def bench_run(
        per_out_ch_quant=per_out_ch,
    )

-    fn = mk.FusedMoEModularKernel(
-        MoEPrepareAndFinalizeNoEP(),
-        CutlassExpertsFp8(
-            out_dtype=a.dtype,
-            e=num_experts,
-            n=n,
-            k=k,
-            quant_config=quant_config,
-            device=w1.device,
-        ),
-    )
-
    # Create CUDA graphs for CUTLASS (match benchmark_moe.py pattern exactly)
    cutlass_stream = torch.cuda.Stream()
    cutlass_graph = torch.cuda.CUDAGraph()
    with torch.cuda.graph(cutlass_graph, stream=cutlass_stream):
        # Capture 10 invocations like benchmark_moe.py
        for _ in range(10):
-            fn(
-                a,
-                w1_fp8q_cutlass,
-                w2_fp8q_cutlass,
-                topk_weights,
-                topk_ids,
+            cutlass_moe_fp8(
+                a=a,
+                w1_q=w1_fp8q_cutlass,
+                w2_q=w2_fp8q_cutlass,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                ab_strides1=ab_strides1,
+                ab_strides2=ab_strides2,
+                c_strides1=c_strides1,
+                c_strides2=c_strides2,
+                quant_config=quant_config,
                activation="silu",
                global_num_experts=num_experts,
            )
@@ -188,8 +255,8 @@ def bench_run(
        torch.cuda.synchronize()

        # Timing
-        start_event = torch.Event(enable_timing=True)
-        end_event = torch.Event(enable_timing=True)
+        start_event = torch.cuda.Event(enable_timing=True)
+        end_event = torch.cuda.Event(enable_timing=True)

        latencies = []
        for _ in range(num_iters):
@@ -230,10 +297,6 @@ def bench_run(


 def main(args):
-    # Initialize workspace manager (required for CUTLASS MoE kernels)
-    device = torch.device("cuda:0")
-    init_workspace_manager(device)
-
    print("Benchmarking models:")
    for i, model in enumerate(args.models):
        print(f"[{i}]  {model}")
--- a/benchmarks/kernels/benchmark_device_communicators.py
+++ b/benchmarks/kernels/benchmark_device_communicators.py
@@ -293,7 +293,7 @@ class CommunicatorBenchmark:
                    graph = torch.cuda.CUDAGraph()
                    graph_pool = torch.cuda.graph_pool_handle()
                    set_graph_pool_id(graph_pool)
-                    with torch.cuda.graph(graph, pool=graph_pool, stream=stream):
+                    with torch.cuda.graph(graph, pool=graph_pool):
                        for _ in range(CUDA_GRAPH_CAPTURE_CYCLES):
                            allreduce_fn(graph_input)

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Lucas Wilkinson	275de34170	[BugFix] Fix false assertion with spec-decode=[2,4,..] and TP>2 (#29036 ) Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> (cherry picked from commit `8f4f77a727`)	2025-11-19 14:11:21 -08:00
Julien Denize	fa3ffb4365	[BugFix] Ray with multiple nodes (#28873 ) Signed-off-by: Julien Denize <julien.denize@mistral.ai> (cherry picked from commit `cdeec2e606`)	2025-11-19 14:11:08 -08:00
Lucas Wilkinson	6d5974369c	[BugFix] Fix async-scheduling + FlashAttn MLA (#28990 ) Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> (cherry picked from commit `48fc8b1e59`)	2025-11-19 14:10:50 -08:00
Johnny	0ce9990d2c	[NVIDIA] Guard SM100 CUTLASS MoE macro to SM100 builds v2 (#28938 ) Signed-off-by: johnnynunez <johnnynuca14@gmail.com> Signed-off-by: Johnny <johnnynuca14@gmail.com> (cherry picked from commit `49ef847aa8`)	2025-11-19 14:10:37 -08:00
				`@@ -1 +0,0 @@`
				`Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml`